WO2023226733A1 - Vehicle scene data acquisition method and apparatus, storage medium and electronic device - Google Patents

Abstract

Disclosed in embodiments of the present disclosure are a vehicle scene data acquisition method and apparatus, a storage medium, and an electronic device. The method comprises: in the vehicle running process, obtaining target data determined by an automatic driving system of the vehicle, wherein the target data comprises at least one of the following: a control instruction of the automatic driving system on the vehicle, sensing data sensed by the automatic driving system for the environment where the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and prediction planning data predicted by the automatic driving system; determining whether the vehicle is in a target scene according to the target data; and under the condition that it is determined that the vehicle is in the target scene, collecting vehicle scene data of the vehicle in the target scene.

Description

车辆场景数据的采集方法、装置、存储介质及电子设备Vehicle scene data collection methods, devices, storage media and electronic equipment

交叉援引cross-citation

本公开要求于2022年05月27日提交中国专利局、申请号202210590860.X、申请名称“车辆场景数据的采集方法、装置、存储介质及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。This disclosure requests the priority of the Chinese patent application filed with the China Patent Office on May 27, 2022, with application number 202210590860. incorporated herein by reference.

技术领域Technical field

本公开涉及通信领域，具体而言，涉及一种车辆场景数据的采集方法、装置、存储介质及电子设备。The present disclosure relates to the field of communications, and specifically, to a vehicle scene data collection method, device, storage medium and electronic device.

背景技术Background technique

随着科技的高速发展，车辆自动驾驶领域发展迅速，但自动驾驶可以分为不同的等级，越高级别的自动驾驶，可以适应的场景越多，即可以做到不限制场景实现自动驾驶，即高级别自动驾驶***的开发应以无边界限制的场景为验证假设，具备对复杂环境及陌生、突发等场景的覆盖能力。With the rapid development of science and technology, the field of vehicle automatic driving is developing rapidly, but automatic driving can be divided into different levels. The higher the level of automatic driving, the more scenarios it can adapt to, that is, automatic driving can be achieved without limiting the scenarios, that is, The development of high-level autonomous driving systems should be based on scenarios with no boundary restrictions as verification assumptions, and should have the ability to cover complex environments and unfamiliar and unexpected scenarios.

自动驾驶算法需要应对的场景较多且复杂，算法生命周期较长，需要长时间迭代优化，但现有测试自动驾驶***的过程中，都是按照固定的测试场景进行测试，发现自动驾驶算法无法覆盖当前工况的情况下，测试人员手动记录问题，优化软件后，再进行测试，效率较低，也会有一些边缘场景无法识别测试，造成无法发现软件的全部问题。即现有场景数据的采集都是人为模拟场景，进而测试人员手动触发场景的收集，使得可获取到的场景数据较少，且缺乏真实性。The autonomous driving algorithm needs to deal with many and complex scenarios. The algorithm has a long life cycle and requires long-term iterative optimization. However, in the current process of testing the autonomous driving system, the test is based on fixed test scenarios, and it is found that the autonomous driving algorithm cannot When covering the current working conditions, testers manually record problems, optimize the software, and then test, which is inefficient. There will also be some edge scenarios where the test cannot be recognized, resulting in the inability to discover all problems with the software. That is, the collection of existing scene data is all artificially simulated scenes, and testers manually trigger the collection of scenes, resulting in less scene data that can be obtained and lack of authenticity.

针对相关技术，获取到的车辆场景数据较少的问题，目前尚未提出有效的解决方案。Regarding related technologies, there is currently no effective solution to the problem of obtaining less vehicle scene data.

因此，有必要对相关技术予以改良以克服相关技术中的所述缺陷。Therefore, it is necessary to improve the related technology to overcome the defects in the related technology.

发明内容 Contents of the invention

有鉴于此，本公开的目的在于提供了一种车辆场景数据的采集方法、装置、存储介质及电子设备，以至少解决获取到的车辆场景数据较少的技术问题。In view of this, the purpose of the present disclosure is to provide a vehicle scene data collection method, device, storage medium and electronic device, so as to at least solve the technical problem of less vehicle scene data being obtained.

为了实现上述目的，第一方面，本公开提供了一种车辆场景数据的采集方法，所述方法包括：在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；根据所述目标数据确定所述车辆是否处于目标场景；在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。In order to achieve the above object, in a first aspect, the present disclosure provides a method for collecting vehicle scene data. The method includes: during the driving process of the vehicle, acquiring target data determined by the automatic driving system of the vehicle, wherein: The target data includes at least one of the following: control instructions of the vehicle by the automatic driving system, sensing data of the environment in which the vehicle is located by the automatic driving system, and the vehicle determined by the automatic driving system. The positioning data and the prediction planning data predicted by the automatic driving system; determine whether the vehicle is in the target scene according to the target data; when it is determined that the vehicle is in the target scene, collect the location of the vehicle in the target scene. Vehicle scene data in the target scene.

在一个示例性的实施例中，在所述目标数据包括所述控制指令的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：在确定所述车辆处于自动驾驶模式、且所述控制指令指示将所述车辆在第一方向上的加速度设置为第一加速度的情况下，确定所述车辆处于行驶异常场景，其中，所述第一方向为所述车辆的前进方向，所述第一加速度的值超过第一阈值；在确定所述车辆处于自动驾驶模式、所述控制指令指示将所述车辆在第二方向的加速度设置为第二加速度、且指示将所述车辆的轮胎的转角变化率设置为目标转角变化率的情况下，确定所述车辆处于行驶异常场景，其中，所述第二加速度超过第二阈值，所述目标转角变化率超过第三阈值，所述第二方向与所述第二方向之间的夹角为预设夹角；在确定所述车辆处于人工驾驶模式的情况下，确定目标对象对车辆下发的目标控制指令；将所述目标控制指令与所述目标数据中的控制指令进行比对，并在所述目标控制指令与所述控制指令的相似度小于第四阈值的情况，确定所述车辆处于预测控制指令异常场景；其中，所述目标场景包括：所述行驶异常场景，所述预测控制指令异常场景。In an exemplary embodiment, when the target data includes the control instruction, determining whether the vehicle is in a target scene according to the target data includes: determining that the vehicle is in an autonomous driving mode, and When the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scene, wherein the first direction is the forward direction of the vehicle, so The value of the first acceleration exceeds the first threshold; after determining that the vehicle is in the automatic driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration, and instructs to set the vehicle's tires to When the turning angle change rate is set as the target turning angle change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target turning angle changing rate exceeds a third threshold, and the second The angle between the direction and the second direction is a preset angle; when it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; combine the target control instruction with The control instructions in the target data are compared, and when the similarity between the target control instruction and the control instruction is less than a fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario; wherein, the target Scenarios include: the abnormal driving scene and the abnormal predictive control instruction scene.

在一个示例性的实施例中，在所述目标数据包括所述感知数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：在根据所述感知数据确定所述自动驾驶***的感知模块感知到的第一障碍物的类型在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；在根据所述感知数据确定第一障碍物的标识在第一预设时长内发生 变化的情况下，确定所述车辆处于感知模块异常场景；在根据所述感知数据确定第一障碍物的移动速度在第一预设时长内的变化量超过第五阈值的情况下，确定所述车辆处于感知模块异常场景；在根据所述感知数据确定所述感知模块感知到的第二障碍物的位置在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；在根据所述感知数据确定所述感知模块感知到的第三障碍物的位置与所述车辆的位置重叠的情况下，确定所述车辆处于感知模块异常场景；在所述感知数据为预设感知数据，以及所述车辆的车身数据为预设车身数据的情况下，确定所述车辆处于预设场景；其中，所述目标场景包括：所述感知模块异常场景，所述预设场景。In an exemplary embodiment, when the target data includes the perception data, determining whether the vehicle is in a target scene according to the target data includes: determining the automatic driving according to the perception data. When the type of the first obstacle sensed by the sensing module of the system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; when it is determined based on the sensing data that the identification of the first obstacle is in Occurs within the first preset time period If there is a change, it is determined that the vehicle is in an abnormal scene of the sensing module; if it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the The vehicle is in an abnormal scene of the sensing module; when it is determined according to the sensing data that the position of the second obstacle sensed by the sensing module changes within a first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module; when the sensing data is the preset sensing data, and when the body data of the vehicle is preset body data, it is determined that the vehicle is in a preset scene; wherein the target scene includes: an abnormal scene of the sensing module and the preset scene.

在一个示例性的实施例中，在所述目标数据包括所述定位数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：在根据所述定位数据确定所述车辆的位置在第二预设时长内的变化量超过第六阈值的情况下，确定所述车辆处于定位模块异常场景；在根据所述定位数据确定所述车辆的移动方向在所述第二预设时长内的变化量超过第七阈值的情况下，确定所述车辆处于定位模块异常场景；在根据所述定位数据确定所述车辆的位置在所述第二预设时长内未发生变化，但所述车辆的速度为目标速度的情况下、确定所述车辆处于定位模块异常场景；其中，所述目标场景包括：所述定位模块异常场景。In an exemplary embodiment, when the target data includes the positioning data, determining whether the vehicle is in a target scene according to the target data includes: determining the location of the vehicle according to the positioning data. When the change in position within the second preset time period exceeds the sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; when it is determined based on the positioning data that the moving direction of the vehicle is within the second preset time period. If the change amount exceeds the seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period, but the When the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module; wherein the target scene includes: an abnormal scene of the positioning module.

在一个示例性的实施例中，在所述目标数据包括所述预测规划数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：在所述预测规划数据用于指示第三障碍物的预测移动状态的情况下，确定所述预测移动状态与所述第三障碍物的目标移动状态的相似度，并在所述相似度小于第八阈值的情况下，确定所述车辆处于预测规划模块异常场景，其中，所述目标移动状态为对所述第三障碍物进行检测后所得到的移动状态；在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹的可行性，并在所述可行性小于第九阈值的情况下，确定所述车辆处于预测规划模块异常场景；在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹上是否具有障碍物，并在确定所述规划移动轨迹上具有障碍物的情况下，确定所述车辆处于预测规划模块异常场景；在所述预测规划数据用于指示所述自动驾驶***无 法控制所述车辆的情下，确定所述车辆处于待人工控制场景；其中，所述目标场景包括：所述预测规划模块异常场景，所述待人工控制场景。In an exemplary embodiment, when the target data includes the predictive planning data, determining whether the vehicle is in a target scenario according to the target data includes: when the predictive planning data is used to indicate the first In the case of the predicted movement state of three obstacles, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine the vehicle In an abnormal scenario of the prediction planning module, the target movement state is the movement state obtained after detecting the third obstacle; when the prediction planning data is used to indicate the planned movement trajectory of the vehicle , determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine that the vehicle is in an abnormal scenario of the prediction planning module; where the prediction planning data is used to indicate the planning of the vehicle In the case of a movement trajectory, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine that the vehicle is in an abnormal scenario of the prediction planning module; in the prediction planning Data used to indicate that the autonomous driving system is not Under the condition that the vehicle cannot be controlled, it is determined that the vehicle is in a scene to be manually controlled; wherein the target scene includes: the abnormal scene of the prediction planning module and the scene to be manually controlled.

在一个示例性的实施例中，采集所述车辆在所述目标场景下的车辆场景数据，包括：采集所述车辆在预设时间段内的车身数据，其中，在所述预设时间段内，所述车辆处于所述目标场景；采集所述车辆在所述预设时间段内的底盘数据；采集所述车辆在所述预设时间段内，所述自动驾驶***产生的过程数据以及控制指令数据；采集所述车辆在所述预设时间段内通过图像采集装置以及雷达传感器确定的所述车辆所处环境的感知数据。In an exemplary embodiment, collecting vehicle scene data of the vehicle in the target scene includes: collecting body data of the vehicle within a preset time period, wherein within the preset time period, , the vehicle is in the target scene; the chassis data of the vehicle is collected within the preset time period; the process data and control data generated by the automatic driving system of the vehicle during the preset time period are collected Instruction data: Collect the sensing data of the environment in which the vehicle is located, determined by the image acquisition device and the radar sensor within the preset time period.

在一个示例性的实施例中，采集所述车辆在所述目标场景下的车辆场景数据之后，所述方法还包括：将所述车辆场景数据发送至云端服务器，以使所述云端服务器根据所述车辆场景数据调整所述自动驾驶***的算法；获取所述云端服务器调整后的目标算法，并将所述自动驾驶***的算法更新为所述目标算法。In an exemplary embodiment, after collecting vehicle scene data of the vehicle in the target scene, the method further includes: sending the vehicle scene data to a cloud server, so that the cloud server can The vehicle scene data adjusts the algorithm of the automatic driving system; obtains the adjusted target algorithm from the cloud server, and updates the algorithm of the automatic driving system to the target algorithm.

第二方面，本公开还提供了一种车辆场景数据的采集装置，所述装置包括：获取模块，设置为在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；确定模块，设置为根据所述目标数据确定所述车辆是否处于目标场景；采集模块，设置为在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。In a second aspect, the present disclosure also provides a device for collecting vehicle scene data. The device includes: an acquisition module configured to acquire target data determined by the automatic driving system of the vehicle while the vehicle is driving, wherein, The target data includes at least one of the following: the control instructions of the automatic driving system to the vehicle, the sensing data of the environment in which the vehicle is located, the sensing data determined by the automatic driving system. The positioning data of the vehicle and the prediction planning data predicted by the automatic driving system; a determination module configured to determine whether the vehicle is in the target scene according to the target data; an acquisition module configured to determine whether the vehicle is in the target scene In the case of , collect vehicle scene data of the vehicle in the target scene.

第三方面，本公开还提供了一种计算机可读存储介质，所述计算机可读存储介质包括存储的程序，其中，在所述程序运行时控制所述计算机可读存储介质所在设备执行上述任一项技术方案中所述的车辆场景数据的采集方法。In a third aspect, the present disclosure also provides a computer-readable storage medium. The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to perform any of the above. A method for collecting vehicle scene data described in a technical solution.

第四方面，本公开还提供了一种电子设备，所述电子设备包括一个或多个处理器；存储装置，设置为存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器实现设置为运行程序，其中，所述程序被设置为运行时执行上述任一项技术方案中所述的车辆场景数据的采集方法。In a fourth aspect, the present disclosure also provides an electronic device, the electronic device includes one or more processors; a storage device configured to store one or more programs, when the one or more programs are processed by the one When executed by or multiple processors, the one or more processors are configured to run a program, wherein the program is configured to execute the vehicle scene data collection method described in any of the above technical solutions when running. .

本公开，在车辆行驶的过程中，通过车辆自动判断是否在设定的目标 场景下，进而触发数据采集，由于车辆识别场景的能力比人工识别的场景的能力更强，进而车辆可以识别的场景更多，并且车辆在行驶时所处的场景具有多样性，进而可以收集到大量的真实、多样的场景数据，进而解决了获取到的车辆场景数据较少的问题。In the present disclosure, during the driving process of the vehicle, the vehicle automatically determines whether it is within the set target. Scenarios trigger data collection. Since the vehicle's ability to recognize scenes is stronger than that of manual recognition, the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, so it can collect A large amount of real and diverse scene data solves the problem of less vehicle scene data being obtained.

为使本公开的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。In order to make the above-mentioned objects, features and advantages of the present disclosure more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

附图说明Description of the drawings

此处所说明的附图用来提供对本公开的进一步理解，构成本申请的一部分，本公开的示意性实施例及其说明设置为解释本公开，并不构成对本公开的不当限定。在附图中：The drawings described here are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are provided to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the attached picture:

图1是本公开实施例的车辆场景数据的采集方法的计算机终端的硬件结构框图；Figure 1 is a hardware structure block diagram of a computer terminal of a vehicle scene data collection method according to an embodiment of the present disclosure;

图2是根据本公开实施例的车辆场景数据的采集方法的流程图；Figure 2 is a flow chart of a vehicle scene data collection method according to an embodiment of the present disclosure;

图3是根据本公开实施例的车端数据采集单元的硬件架构；Figure 3 is the hardware architecture of a vehicle-side data collection unit according to an embodiment of the present disclosure;

图4是根据本公开实施例的数据采集触发机制图(一)；Figure 4 is a data collection triggering mechanism diagram (1) according to an embodiment of the present disclosure;

图5是根据本公开实施例的数据采集触发场景示意图；Figure 5 is a schematic diagram of a data collection triggering scenario according to an embodiment of the present disclosure;

图6是根据本公开实施例的数据采集触发机制图(二)；Figure 6 is a data collection triggering mechanism diagram (2) according to an embodiment of the present disclosure;

图7是根据本公开实施例的车辆场景数据的采集装置的结构框图。FIG. 7 is a structural block diagram of a device for collecting vehicle scene data according to an embodiment of the present disclosure.

具体实施方式Detailed ways

下面，结合附图对本公开的具体实施例进行详细的描述，但不作为本公开的限定。Below, specific embodiments of the present disclosure are described in detail with reference to the accompanying drawings, but are not intended to limit the disclosure.

应理解的是，可以对此处公开的实施例做出各种修改。因此，上述说明书不应该视为限制，而是作为实施例的范例。本领域的技术人员将想到在本公开的范围和精神内的其他修改。It will be understood that various modifications may be made to the embodiments disclosed herein. Therefore, the above description should not be regarded as limiting, but as examples of embodiments. Other modifications within the scope and spirit of this disclosure will occur to those skilled in the art.

包含在说明书中并构成说明书的一部分的附图示出了本公开的实施例，并且与上面给出的对本公开的大致描述以及下面给出的对实施例的详细描述一起设置为解释本公开的原理。The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, are intended to explain the disclosure. principle.

通过下面参照附图对给定为非限制性实例的实施例的优选形式的描述，本公开的这些和其它特性将会变得显而易见。These and other features of the present disclosure will become apparent from the following description of preferred forms of embodiments given as non-limiting examples with reference to the accompanying drawings.

还应当理解，尽管已经参照一些具体实例对本公开进行了描述，但本领域技术人员能够确定地实现本公开的很多其它等效形式，它们具有如权 利要求所述的特征并因此都处于借此所限定的保护范围内。It should also be understood that, although the disclosure has been described with reference to a few specific examples, those skilled in the art will be able to undoubtedly implement many other equivalent forms of the disclosure having the rights to The features described in the claims are therefore within the scope of protection defined thereby.

当结合附图时，鉴于以下详细说明，本公开的上述和其他方面、特征和优势将变得更为显而易见。The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.

此后参照附图描述本公开的具体实施例；然而，应当理解，所公开的实施例是本公开的实例，其可采用多种方式实施。熟知和/或重复的功能和结构并未详细描述以避免不必要或多余的细节使得本公开模糊不清。因此，本文所公开的具体的结构性和功能性细节并非意在限定，而是作为权利要求的基础和代表性基础用于教导本领域技术人员以实质上任意合适的详细结构多样地使用本公开。Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are examples of the disclosure, which can be implemented in various ways. Well-known and/or repeated functions and structures have not been described in detail to avoid obscuring the disclosure with unnecessary or redundant detail. Therefore, specific structural and functional details disclosed herein are not intended to be limiting, but rather serve as a basis and representative basis for the claims, and serve as a basis for teaching one skilled in the art to variously employ the present disclosure in substantially any suitable detailed structure. .

需要说明的是，本公开的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本公开的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、***、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terms "first", "second", etc. in the description and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances so that the embodiments of the disclosure described herein can be practiced in sequences other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus that encompasses a series of steps or units and need not be limited to those explicitly listed. Those steps or elements may instead include other steps or elements not expressly listed or inherent to the process, method, product or apparatus.

本说明书可使用词组“在一种实施例中”、“在另一个实施例中”、“在又一实施例中”或“在其他实施例中”，其均可指代根据本公开的相同或不同实施例中的一个或多个。This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in further embodiments," which may each refer to the same thing in accordance with the present disclosure. or one or more of the different embodiments.

下面结合附图和具体的实施例对本公开作进一步的说明。The present disclosure will be further described below in conjunction with the accompanying drawings and specific embodiments.

本申请实施例中所提供的方法实施例可以在计算机终端或者类似的运算装置中执行。以运行在计算机终端上为例，图1是本公开实施例的车辆场景数据的采集方法的计算机终端的硬件结构框图。如图1所示，计算机终端可以包括一个或多个(图1中示出一个)处理器102(处理器102可以包括但不限于微处理器(Microprocessor Unit，简称是MPU)或可编程逻辑器件(Programmable logic device，简称是PLD))和设置为存储数据的存储器104，在一个示例性实施例中，上述计算机终端还可以包括设置为通信功能的传输设备106以及输入输出设备108。本领域普通技术人员可以理解，图1所示的结构为示意，其并不对上述计算机终端的结构造成限定。例如，计算机终端还可包括比图1中所示更多或者更少的组件， 或者具有与图1所示等同功能或比图1所示功能更多的不同的配置。The method embodiments provided in the embodiments of this application can be executed in a computer terminal or similar computing device. Taking running on a computer terminal as an example, FIG. 1 is a hardware structure block diagram of a computer terminal for a vehicle scene data collection method according to an embodiment of the present disclosure. As shown in FIG. 1 , the computer terminal may include one or more (one is shown in FIG. 1 ) processors 102 (the processor 102 may include but is not limited to a microprocessor unit (MPU for short) or a programmable logic device. (Programmable logic device, referred to as PLD)) and a memory 104 configured to store data. In an exemplary embodiment, the above-mentioned computer terminal may also include a transmission device 106 configured to have a communication function and an input and output device 108. Persons of ordinary skill in the art can understand that the structure shown in FIG. 1 is schematic and does not limit the structure of the above-mentioned computer terminal. For example, a computer terminal may also include more or fewer components than shown in Figure 1, Or a different configuration with equivalent functions or more functions than those shown in FIG. 1 .

存储器104可设置为存储计算机程序，例如，应用软件的软件程序以及模块，如本公开实施例中的车辆场景数据的采集方法对应的计算机程序，处理器102通过运行存储在存储器104内的计算机程序，从而执行各种功能应用以及数据处理，即实现上述的方法。存储器104可包括高速随机存储器，还可包括非易失性存储器，如一个或者多个磁性存储装置、闪存、或者其他非易失性固态存储器。在一些实例中，存储器104可进一步包括相对于处理器102远程设置的存储器，这些远程存储器可以通过网络连接至计算机终端。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory 104 may be configured to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle scene data collection method in the embodiment of the present disclosure. The processor 102 runs the computer program stored in the memory 104 , thereby executing various functional applications and data processing, that is, implementing the above method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely relative to the processor 102, and these remote memories may be connected to the computer terminal through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.

传输装置106设置为经由一个网络接收或者发送数据。上述的网络具体实例可包括计算机终端的通信供应商提供的无线网络。在一个实例中，传输装置106包括一个网络适配器(Network Interface Controller，简称为NIC)，其可通过基站与其他网络设备相连从而可与互联网进行通讯。在一个实例中，传输装置106可以为射频(Radio Frequency，简称为RF)模块，其设置为通过无线方式与互联网进行通讯。The transmission device 106 is arranged to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC for short), which can be connected to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (Radio Frequency, RF for short) module, which is configured to communicate with the Internet wirelessly.

由于自动驾驶车辆在行驶时可以收集大量的真实场景数据，为从大量的场景数据中高效地将自动驾驶需求的场景筛选出来，本公开提出一种车辆场景数据采集方法，分别在自动驾驶和人工驾驶状态下进行真实场景数据采集，自动驾驶***持续运行，在自动驾驶模式下输出指令控制车辆行驶，在人工驾驶模式下输出虚拟指令与实际驾驶员对比差异，通过自动驾驶异常场景识别、人车差异对比分析以及设定的感兴趣场景对车端真实场景数据进行有效触发并采集。Since self-driving vehicles can collect a large amount of real scene data while driving, in order to efficiently filter out the scenes required by self-driving from a large amount of scene data, this disclosure proposes a vehicle scene data collection method, which can be used in automatic driving and manual driving respectively. Real-scenario data collection is carried out under driving conditions. The autonomous driving system continues to run. In autonomous driving mode, instructions are output to control vehicle driving. In manual driving mode, virtual instructions are output to compare the differences with the actual driver. Through automatic driving abnormal scene recognition, human and vehicle Difference comparison analysis and set scenes of interest effectively trigger and collect real scene data on the vehicle side.

在本实施例中提供了一种车辆场景数据的采集方法，图2是根据本公开实施例的车辆场景数据的采集方法的流程图，该流程包括如下步骤：In this embodiment, a vehicle scene data collection method is provided. Figure 2 is a flow chart of a vehicle scene data collection method according to an embodiment of the present disclosure. The process includes the following steps:

步骤S202：在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据； Step S202: While the vehicle is driving, obtain the target data determined by the automatic driving system of the vehicle, where the target data includes at least one of the following: the control instructions of the automatic driving system for the vehicle, the Sensing data perceived by the autonomous driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the autonomous driving system, and predictive planning data predicted by the autonomous driving system;

作为一个可选的示例，上述控制指令包括但不限于：控制车辆加速，控制车辆刹车、减速，控制车辆转弯，控制车辆掉头，控制车辆打开灯光等控制指令。As an optional example, the above control instructions include but are not limited to: control vehicle acceleration, control vehicle braking and deceleration, control vehicle turning, control vehicle U-turn, control vehicle turn on lights and other control instructions.

作为一个可选的示例，上述感知数据包括但不限于：感知车辆周围具有的障碍物的数量，类型，标识、速度、位置、行驶状态等等。As an optional example, the above-mentioned sensing data includes but is not limited to: sensing the number, type, identification, speed, location, driving status, etc. of obstacles around the vehicle.

作为一种可选的示例，上述定位数据包括但不限于：车辆的位置，车辆的移动方向(航向)等。As an optional example, the above positioning data includes but is not limited to: the position of the vehicle, the moving direction (heading) of the vehicle, etc.

作为一种可选的示例，上述预测规划数据包括但不限于：预测到的障碍物的移动状态(例如，障碍物向xx方向以x速度移动)，规划的车辆的出行轨迹等等。As an optional example, the above-mentioned predictive planning data includes but is not limited to: the predicted movement state of the obstacle (for example, the obstacle moves in the x direction at x speed), the planned travel trajectory of the vehicle, and so on.

步骤S204：根据所述目标数据确定所述车辆是否处于目标场景；Step S204: Determine whether the vehicle is in a target scene according to the target data;

需要说明的是，目标场景为测试人员想要收集车辆场景数据的场景，目标场景包括但不限于：行驶异常场景、预测控制指令异常场景、感知模块异常场景、测试人员自定义的预设场景、定位模块异常场景、预测规划模块异常场景、待人工控制场景。下文将对这些场景进行具体说明，在此不进行赘述。It should be noted that the target scenario is the scenario where the tester wants to collect vehicle scene data. The target scenario includes but is not limited to: abnormal driving scenarios, abnormal predictive control instruction scenarios, abnormal sensing module scenarios, preset scenarios customized by the tester, Abnormal scenarios of the positioning module, abnormal scenarios of the prediction and planning module, and scenarios to be manually controlled. These scenarios will be described in detail below and will not be described in detail here.

步骤S206：在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。Step S206: When it is determined that the vehicle is in the target scene, collect vehicle scene data of the vehicle in the target scene.

作为一种可选的示例，采集车辆在所述目标场景下的车辆场景数据，可以通过以下方式实现：As an optional example, collecting vehicle scene data of the vehicle in the target scene can be achieved in the following ways:

采集所述车辆在预设时间段内的车身数据，其中，在所述预设时间段内，所述车辆处于所述目标场景；采集所述车辆在所述预设时间段内的底盘数据；采集所述车辆在所述预设时间段内，所述自动驾驶***产生的过程数据以及控制指令数据；采集所述车辆在所述预设时间段内通过图像采集装置以及雷达传感器确定的所述车辆所处环境的感知数据。Collecting body data of the vehicle within a preset time period, wherein the vehicle is in the target scene during the preset time period; collecting chassis data of the vehicle within the preset time period; Collect the process data and control instruction data generated by the automatic driving system of the vehicle within the preset time period; collect the image acquisition device and radar sensor determined by the vehicle within the preset time period. Perception data of the vehicle’s environment.

需要说明的是，车辆场景数据包括：车身数据、底盘数据、自动驾驶***产生的过程数据以及控制指令数据、感知数据。It should be noted that vehicle scene data includes: body data, chassis data, process data generated by the autonomous driving system, control instruction data, and perception data.

具体的，图3是根据本公开实施例的车端数据采集单元的硬件架构，可以通过如图3所示的车端数据采集单元进行车辆场景数据的采集，其中，车端数据采集单元包含：数据采集触发计算单元，数据存储单元；Specifically, Figure 3 is the hardware architecture of a vehicle-side data collection unit according to an embodiment of the present disclosure. Vehicle scene data can be collected through the vehicle-side data collection unit as shown in Figure 3, where the vehicle-side data collection unit includes: Data acquisition triggering calculation unit, data storage unit;

车端数据采集单元的输入包含： The input of the vehicle-side data collection unit includes:

(1)通过网关传输进来的车辆的车身数据、底盘数据等信息；(1) Vehicle body data, chassis data and other information transmitted through the gateway;

(2)自动驾驶***的自动驾驶控制单元产生的过程数据和控制指令数据；(2) Process data and control instruction data generated by the automatic driving control unit of the automatic driving system;

(3)车辆通过图像采集装置(例如：摄像头)、雷达传感器(例如：激光雷达)感知到的感知数据。(3) Sensing data perceived by the vehicle through image acquisition devices (such as cameras) and radar sensors (such as lidar).

作为一种可选的示例，在执行上述步骤S206之后，还需要将所述车辆场景数据发送至云端服务器，以使所述云端服务器根据所述车辆场景数据调整所述自动驾驶***的算法；获取所述云端服务器调整后的目标算法，并将所述自动驾驶***的算法更新为所述目标算法。As an optional example, after performing the above step S206, the vehicle scene data also needs to be sent to the cloud server, so that the cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data; obtain The cloud server adjusts the target algorithm, and updates the algorithm of the autonomous driving system to the target algorithm.

可选的，可以通过如图3所示的通讯模块，将车端数据采集单元中的数据存储单元中存储的车辆场景数据上传至云端服务器中。Optionally, the vehicle scene data stored in the data storage unit in the vehicle-side data acquisition unit can be uploaded to the cloud server through the communication module as shown in Figure 3.

通过上述步骤，在车辆行驶的过程中，通过车辆自动判断是否在设定的目标场景下，进而触发数据采集，由于车辆识别场景的能力比人工识别的场景的能力更强，进而车辆可以识别的场景更多，并且车辆在行驶时所处的场景具有多样性，进而可以收集到大量的真实、多样的场景数据，进而解决了获取到的车辆场景数据较少的问题。Through the above steps, while the vehicle is driving, the vehicle automatically determines whether it is in the set target scene, and then triggers data collection. Since the vehicle's ability to identify scenes is stronger than that of manual identification, the vehicle can then identify There are more scenes, and the scenes in which the vehicle is driving are diverse, so a large amount of real and diverse scene data can be collected, thus solving the problem of less vehicle scene data being obtained.

作为一种可选的示例，在所述目标数据包括所述控制指令的情况下，根据所述目标数据确定所述车辆是否处于目标场景，通过以下方式一至方式三实现：As an optional example, when the target data includes the control instruction, determining whether the vehicle is in the target scene according to the target data is implemented by the following methods one to three:

方式一：在确定所述车辆处于自动驾驶模式、且所述控制指令指示将所述车辆在第一方向上的加速度设置为第一加速度的情况下，确定所述车辆处于行驶异常场景，其中，所述第一方向为所述车辆的前进方向，所述第一加速度的值超过第一阈值，目标场景包括：行驶异常场景；Method 1: When it is determined that the vehicle is in the automatic driving mode and the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scenario, wherein, The first direction is the forward direction of the vehicle, the value of the first acceleration exceeds a first threshold, and the target scene includes: an abnormal driving scene;

需要说明的是，如果控制指令指示将车辆在第一方向上的加速度设置为第一加速度，则说明自动驾驶***要控制车辆急刹车，即当前车辆处于突发极端的状态，即处于行驶异常场景下。It should be noted that if the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it means that the automatic driving system needs to control the vehicle to brake suddenly, that is, the current vehicle is in a sudden extreme state, that is, it is in an abnormal driving scenario. Down.

方式二：在确定所述车辆处于自动驾驶模式、所述控制指令指示将所述车辆在第二方向的加速度设置为第二加速度、且指示将所述车辆的轮胎的转角变化率设置为目标转角变化率的情况下，确定所述车辆处于行驶异常场景，其中，所述第二加速度超过第二阈值，所述目标转角变化率超过第三阈值，所述第二方向与所述第二方向之间的夹角为预设夹角； Method 2: After it is determined that the vehicle is in the autonomous driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction as the second acceleration, and instructs to set the rotation angle change rate of the vehicle's tires as the target rotation angle. In the case of change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target angle change rate exceeds a third threshold, and the second direction is The angle between is the preset angle;

需要说明的是，如果控制指令指示将车辆在第二方向的加速度设置为第二加速度、且指示将车辆的轮胎的转角变化率设置为目标转角变化率，则说明自动驾驶要控制车辆急转向，则当前车辆处于突发极端的状态，即处于行驶异常场景下。It should be noted that if the control instruction indicates that the acceleration of the vehicle in the second direction is set to the second acceleration and the rotation angle change rate of the vehicle's tires is set to the target angle change rate, it means that the automatic driving needs to control the vehicle to turn sharply. Then the current vehicle is in a sudden extreme state, that is, in an abnormal driving scenario.

方式三：在确定所述车辆处于人工驾驶模式的情况下，确定目标对象对车辆下发的目标控制指令；将所述目标控制指令与所述目标数据中的控制指令进行比对，并在所述目标控制指令与所述控制指令的相似度小于第四阈值的情况，确定所述车辆处于预测控制指令异常场景，其中，目标场景包括：预测控制指令异常场景。Method 3: When it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; compare the target control instruction with the control instruction in the target data, and compare the target control instruction with the control instruction in the target data. If the similarity between the target control instruction and the control instruction is less than the fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scene, where the target scene includes: a predictive control instruction abnormal scene.

需要说明的是，在人工驾驶模式下，自动驾驶***模拟运行，其下发的控制指令不直接操控车辆，此时将驾驶员的操作行为和车辆的实际状态与自动驾驶***的控制指令进行差异分析，当二者差异超过设定的第四阈值的时候，则说明确定车辆处于预测控制指令异常场景。It should be noted that in manual driving mode, the automatic driving system simulates operation, and the control instructions issued by it do not directly control the vehicle. At this time, the driver's operating behavior and the actual state of the vehicle are different from the control instructions of the automatic driving system. After analysis, when the difference between the two exceeds the set fourth threshold, it means that the vehicle is determined to be in an abnormal scenario of predictive control instructions.

可选的，如果目标控制指令为控制车辆左转，和控制指令为控制车辆右转，则说明两者的相似度小于第四阈值。Optionally, if the target control instruction is to control the vehicle to turn left, and the control instruction is to control the vehicle to turn right, it means that the similarity between the two is less than the fourth threshold.

作为一种可选的示例，在所述目标数据包括所述感知数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，可以通过以下方式四至方式九确定：As an optional example, when the target data includes the sensing data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods four to nine:

方式四：在根据所述感知数据确定所述自动驾驶***的感知模块感知到的第一障碍物的类型在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景，其中，目标场景包括：感知模块异常场景；Method 4: When it is determined based on the sensing data that the type of the first obstacle sensed by the sensing module of the automatic driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module, Among them, the target scenarios include: abnormal sensing module scenarios;

可选的，第一预设时长可以为1秒、2秒等，第一障碍物为可以移动的障碍物(例如：道路上可以移动的行人、车)，如果第一障碍物的类型在第一预设时长内发生变化(例如，开始是行人，后面是车)，则说明感知到的障碍物的类型跳变，即感知模块异常，车辆处于感知模块异常场景。Optionally, the first preset time period may be 1 second, 2 seconds, etc., and the first obstacle is a movable obstacle (for example: movable pedestrians or vehicles on the road). If the type of the first obstacle is in the first If a change occurs within a preset period of time (for example, there is a pedestrian at the beginning and a car behind), it means that the type of obstacle perceived changes, that is, the sensing module is abnormal and the vehicle is in an abnormal scene of the sensing module.

方式五：在根据所述感知数据确定第一障碍物的标识在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；Method 5: When it is determined based on the sensing data that the identification of the first obstacle changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

需要说明的是，如果第一障碍物的标识在第一预设时长内发生变化(例如：最开始是第一障碍物的标识是车辆1，然后变为了车辆2)，则说明感知到的障碍物的标识跳变，即感知模块异常，车辆处于感知模块异常场景。 It should be noted that if the identity of the first obstacle changes within the first preset time period (for example: initially the identity of the first obstacle is vehicle 1, and then changes to vehicle 2), it means that the perceived obstacle The object's identification jumps, that is, the sensing module is abnormal, and the vehicle is in an abnormal scene of the sensing module.

方式六：在根据所述感知数据确定第一障碍物的移动速度在第一预设时长内的变化量超过第五阈值的情况下，确定所述车辆处于感知模块异常场景；Method 6: When it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the vehicle is in an abnormal scene of the sensing module;

需要说明的是，如果第一障碍物的移动速度在第一预设时长内的变化量超过第五阈值，则说明感知到的障碍物的速度发生跳变，即感知模块异常，车辆处于感知模块异常场景。It should be noted that if the change in the moving speed of the first obstacle exceeds the fifth threshold within the first preset time period, it means that the speed of the perceived obstacle has jumped, that is, the sensing module is abnormal and the vehicle is in the sensing module. Unusual scene.

方式七：在根据所述感知数据确定所述感知模块感知到的第二障碍物的位置在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；Method 7: When it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

需要说明的是，第二障碍物为静止的障碍物(例如，树、房子等)，如果第二障碍物的位置在第一预设时长内发生变化，则说明静止障碍位置跳变，即感知模块异常，车辆处于感知模块异常场景。It should be noted that the second obstacle is a stationary obstacle (for example, a tree, a house, etc.). If the position of the second obstacle changes within the first preset time period, it means that the position of the stationary obstacle jumps, that is, the perception The module is abnormal and the vehicle is in a scenario where the sensing module is abnormal.

方式八：在根据所述感知数据确定所述感知模块感知到的第三障碍物的位置与所述车辆的位置重叠的情况下，确定所述车辆处于感知模块异常场景；Method 8: When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module;

需要说明的是，所述第三障碍物包括所述第一障碍物与所述第二障碍物。It should be noted that the third obstacle includes the first obstacle and the second obstacle.

方式九：在所述感知数据为预设感知数据，以及所述车辆的车身数据为预设车身数据的情况下，确定所述车辆处于预设场景，其中，目标场景包括：预设场景。Method 9: When the sensing data is preset sensing data and the vehicle body data is preset body data, determine that the vehicle is in a preset scene, where the target scene includes: a preset scene.

可选的，自动驾驶***会针对特定场景进行开发，因此需要对特性场景进行数据采集。具体的，可以在云端服务器对感兴趣的预设场景进行配置，并根据具体需求将要需要采集数据的场景下发到车辆，车辆接收到预设场景需求时对该场景进行识别，并触发数据记录，上传到云端服务器。例如，需要收集自动驾驶变道场景数据，即可下发到车端后车端根据感知数据识别出车辆变道行为，然后将变道的整体过程数据记录下来，上传到云端存储应用。Optionally, the autonomous driving system will be developed for specific scenarios, so data collection for characteristic scenarios is required. Specifically, the preset scenes of interest can be configured on the cloud server, and the scenes that need to be collected are sent to the vehicle according to specific needs. When the vehicle receives the preset scene requirements, it will identify the scene and trigger data recording. , upload to the cloud server. For example, if it is necessary to collect autonomous driving lane changing scene data, it can be sent to the car. The car will then identify the vehicle's lane changing behavior based on the sensing data, then record the overall lane changing process data and upload it to the cloud storage application.

预设场景可根据感知、定位和高精地图信息综合判断，例如：(1)道路类型(高速/城市/园区…)、形状(直道/弯道/掉头…)、车道数量、信号灯；(2)障碍物数量、类别、位置、速度等；(3)自车当前速度、加速度、灯光状态等。其中，预设感知数据包括但不限于上述(1)，预设车身 数据包括但不限于上述(3)。Preset scenes can be comprehensively judged based on perception, positioning and high-precision map information, such as: (1) road type (highway/city/park...), shape (straight/curve/U-turn...), number of lanes, traffic lights; (2) ) The number, category, location, speed, etc. of obstacles; (3) The current speed, acceleration, lighting status, etc. of the vehicle. Among them, the preset sensing data includes but is not limited to (1) above, the preset body Data includes but is not limited to (3) above.

作为一种可选的示例，在所述目标数据包括所述定位数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，可以通过以下方式十至方式十二确定：As an optional example, when the target data includes the positioning data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods ten to twelve:

方式十：在根据所述定位数据确定所述车辆的位置在第二预设时长内的变化量超过第六阈值的情况下，确定所述车辆处于定位模块异常场景，其中，所述目标场景包括：所述定位模块异常场景；Method 10: When it is determined based on the positioning data that the change in the position of the vehicle within the second preset time period exceeds the sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module, wherein the target scene includes : Abnormal scenario of the positioning module;

可选的，第二预设时长可以为1秒或者2秒。Optionally, the second preset duration may be 1 second or 2 seconds.

需要说明的是，如果车辆的位置在第二预设时长内的变化量超过第六阈值(例如：从定位北京变化为定位武汉)，则说明车辆自身的位置跳变，即自动驾驶***的定位模块异常，车辆处于定位模块异常场景。It should be noted that if the change in the vehicle's position within the second preset time period exceeds the sixth threshold (for example, from positioning Beijing to positioning Wuhan), it means that the position of the vehicle itself jumps, that is, the positioning of the autonomous driving system. The module is abnormal and the vehicle is in a positioning module abnormality scenario.

方式十一：在根据所述定位数据确定所述车辆的移动方向在所述第二预设时长内的变化量超过第七阈值的情况下，确定所述车辆处于定位模块异常场景；Method 11: When it is determined based on the positioning data that the change in the movement direction of the vehicle within the second preset time period exceeds a seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

需要说明的是，如果车辆的移动方向在所述第二预设时长内的变化量超过第七阈值(例如：从向前行驶变化突然变化为向后行驶)，则说明车辆自身的航向跳变，即自动驾驶***的定位模块异常，车辆处于定位模块异常场景。It should be noted that if the change in the vehicle's moving direction within the second preset time period exceeds the seventh threshold (for example, a sudden change from forward driving to backward driving), it means that the vehicle's own heading jumps. , that is, the positioning module of the automatic driving system is abnormal, and the vehicle is in a positioning module abnormality scenario.

方式十二：在根据所述定位数据确定所述车辆的位置在所述第二预设时长内未发生变化，但所述车辆的速度为目标速度的情况下、确定所述车辆处于定位模块异常场景；Method 12: When it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period, but the speed of the vehicle is the target speed, it is determined that the vehicle is in a positioning module abnormality. Scenes;

需要说明的是，如果车辆的位置没有发生变化，但车辆是具有速度的，则说明自动驾驶***的定位模块异常，车辆处于定位模块异常场景。It should be noted that if the position of the vehicle does not change but the vehicle has speed, it means that the positioning module of the automatic driving system is abnormal and the vehicle is in a positioning module abnormality scenario.

作为一种可选的示例，在所述目标数据包括所述预测数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，可以通过以下方式十三至方式十六确定：As an optional example, when the target data includes the prediction data, determining whether the vehicle is in the target scene based on the target data can be determined through the following methods thirteen to sixteen:

方式十三：在所述预测数据用于指示第三障碍物的预测移动状态的情况下，确定所述预测移动状态与所述第三障碍物的目标移动状态的相似度，并在所述相似度小于第八阈值的情况下，确定所述车辆处于预测规划模块异常场景，其中，所述目标移动状态为对所述第三障碍物进行检测后所得到的移动状态，目标场景包括：预测规划模块异常场景； Method 13: In the case where the predicted data is used to indicate the predicted movement state of the third obstacle, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and determine the similarity between the predicted movement state and the target movement state of the third obstacle. If the degree is less than the eighth threshold, it is determined that the vehicle is in an abnormal scenario of the predictive planning module, wherein the target movement state is the movement state obtained after detecting the third obstacle, and the target scenario includes: predictive planning Module abnormality scenarios;

需要说明的是，如果自动驾驶模块预测的第三障碍物的预测移动状态和检测到的第三障碍物的实际移动状态差异较大，则说明自动驾驶***的预测规划模块异常，即车辆处于预测规划模块异常场景。It should be noted that if the predicted movement state of the third obstacle predicted by the autonomous driving module is significantly different from the actual movement state of the detected third obstacle, it means that the predictive planning module of the autonomous driving system is abnormal, that is, the vehicle is in the predicted state. Planning module abnormal scenarios.

方式十四：在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹的可行性，并在所述可行性小于第九阈值的情况下，确定所述车辆处于预测规划模块异常场景；Method 14: When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine the feasibility of the planned movement trajectory. The vehicle described above is in an abnormal scenario in the predictive planning module;

方式十五：在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹上是否具有障碍物，并在确定所述规划移动轨迹上具有障碍物的情况下，确定所述车辆处于预测规划模块异常场景；Method 15: When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and determine whether there are obstacles on the planned movement trajectory. Next, it is determined that the vehicle is in an abnormal scenario of the prediction planning module;

方式十六：在所述预测规划数据用于指示所述自动驾驶***无法控制所述车辆的情下，确定所述车辆处于待人工控制场景，其中，目标场景包括：待人工控制场景。Method 16: When the predictive planning data is used to indicate that the automatic driving system cannot control the vehicle, determine that the vehicle is in a scenario to be manually controlled, where the target scenario includes a scenario to be manually controlled.

显然，上述所描述的实施例是本公开一部分的实施例，而不是全部的实施例。为了更好的理解上述车辆场景数据的采集方法，以下结合实施例对上述过程进行说明，但不用于限定本公开实施例的技术方案，具体地：Obviously, the above-described embodiments are part of the embodiments of the present disclosure, rather than all embodiments. In order to better understand the above-mentioned vehicle scene data collection method, the above-mentioned process will be described below with reference to embodiments, but this is not intended to limit the technical solutions of the embodiments of the present disclosure, specifically:

为实现自动驾驶车辆充分有效地采集真实场景数据，本公开提出了一种车辆场景数据采集方法，实现在自动驾驶和人工驾驶的模式下都可以触发数据采集，具体分为自动驾驶异常识别、人车差异分析识别和感兴趣场景识别，然后将数据上传至云端服务器中进行存储和应用。In order to realize automatic driving vehicles to fully and effectively collect real scene data, this disclosure proposes a vehicle scene data collection method, which can trigger data collection in both automatic driving and manual driving modes. It is specifically divided into automatic driving anomaly identification, human driving Vehicle difference analysis and identification and interest scene identification, and then upload the data to the cloud server for storage and application.

在车辆位于自动驾驶模式时，图4是根据本公开实施例的数据采集触发机制图(一)，如附图4所示，数据采集触发机制分为两种情况，When the vehicle is in the autonomous driving mode, Figure 4 is a diagram (1) of the data collection trigger mechanism according to an embodiment of the present disclosure. As shown in Figure 4, the data collection trigger mechanism is divided into two situations.

(1)自动驾驶异常场景识别(1) Automatic driving abnormal scene recognition

自动驾驶的异常场景识别主要包含五种情况，图5是根据本公开实施例的数据采集触发场景示意图；Abnormal scene recognition of autonomous driving mainly includes five situations. Figure 5 is a schematic diagram of a data collection triggering scene according to an embodiment of the present disclosure;

具体说明如下：The specific instructions are as follows:

(11)自动驾驶异常接管(相当于上述实施例中待人工控制场景)(11) Automatic driving abnormal takeover (equivalent to the scene to be manually controlled in the above embodiment)

当自动驾驶运行时，出现***不能掌控的场景，会提示驾驶员人工接管，根据接管信号可对此类场景触发数据记录。When autonomous driving is running, if a scene cannot be controlled by the system, the driver will be prompted to take over manually. Data recording for such scenes can be triggered based on the takeover signal.

(12)自动驾驶控制指令异常(相当于上述实施例中的行驶异常场景) (12) Automatic driving control command abnormality (equivalent to the abnormal driving scene in the above embodiment)

当自动驾驶运行出现急刹车时，说明车辆当前处于异常或极端场景，需要记录数据进一步分析，可根据纵向(相当于上述实施例中的第一方向)加速度超过一定阈值，来触发数据记录。When sudden braking occurs during autonomous driving operation, it means that the vehicle is currently in an abnormal or extreme scene and needs to record data for further analysis. Data recording can be triggered based on the longitudinal (equivalent to the first direction in the above embodiment) acceleration exceeding a certain threshold.

当自动驾驶运行出现急转向时，说明当前为异常或极端场景，需要记录数据进一步分析，可根据侧向(相当于上述实施例中的第二方向)加速度超过一定阈值，并且转角的变化率超过一定阈值，来触发数据记录。When a sharp turn occurs during autonomous driving operation, it indicates that the current situation is an abnormal or extreme scene, and the data needs to be recorded for further analysis. According to the lateral (equivalent to the second direction in the above embodiment) acceleration exceeds a certain threshold, and the change rate of the turning angle exceeds A certain threshold is used to trigger data recording.

(13)感知数据异常(相当于上述实施例中的感知模块异常场景)(13) Sensing data abnormality (equivalent to the sensing module abnormality scenario in the above embodiment)

对一段时间的历史感知数据进行综合判断，当发现感知的障碍物的类型跳变、速度跳变、障碍物ID跳变、静止障碍位置跳变等情形时，触发数据记录。Comprehensive judgment is made on the historical sensing data for a period of time, and data recording is triggered when it is found that the type of perceived obstacle changes, speed jumps, obstacle ID jumps, stationary obstacle position jumps, etc.

当障碍物识别位置出现与自车重叠等常识错误时，触发数据记录。Data recording is triggered when common sense errors such as overlapping of the obstacle recognition position with the own vehicle occur.

(14)定位数据异常(相当于上述实施例中的定位模块异常场景)(14) Positioning data abnormality (equivalent to the positioning module abnormality scenario in the above embodiment)

对一段时间的历史定位数据进行综合判断，当发现自车位置跳变、自车航向跳变、有车速但自车位置不变等情形时，触发数据记录。Comprehensive judgment is made on the historical positioning data for a period of time, and data recording is triggered when it is found that the vehicle's position jumps, the vehicle's heading jumps, the vehicle speed is constant but the vehicle's position remains unchanged, etc.

(15)预测规划数据异常(相当于上述实施例中的规划预测模块异常场景)(15) Forecast planning data anomaly (equivalent to the planning prediction module abnormality scenario in the above embodiment)

对一段时间的历史预测规划数据进行综合判断，当发现障碍物预测与实际障碍真值差异较大、规划不出可行轨迹、规划轨迹与障碍碰撞等异常时，触发数据记录。Comprehensive judgment is made on historical prediction and planning data for a period of time. When it is found that there is a large difference between the obstacle prediction and the actual obstacle true value, a feasible trajectory cannot be planned, or the planned trajectory collides with obstacles, data recording is triggered.

需要说明的是，自动驾驶***一般包含感知、定位、预测规划模块，当自动驾驶***中的功能模块出现异常时，触发数据记录。It should be noted that autonomous driving systems generally include perception, positioning, and predictive planning modules. When functional modules in the autonomous driving system are abnormal, data recording is triggered.

(2)感兴趣场景(相当于上述实施例中的预设场景)识别(2) Identification of interesting scenes (equivalent to the preset scenes in the above embodiment)

自动驾驶会针对特定场景进行开发，因此需要对特性场景进行数据采集。如附图4所示，在云端对感兴趣场景进行配置，并根据具体需求将要采集的场景下发到车辆，车辆接收到感兴趣场景需求时对该场景进行识别，并触发数据记录，上传到云端。例如，需要收集自动驾驶变道场景数据，即可下发到车端后车端根据感知数据识别出车辆变道行为，然后将变道的整体过程数据记录下来，上传到云端存储应用。 Autonomous driving will be developed for specific scenarios, so data collection for characteristic scenarios is required. As shown in Figure 4, the scene of interest is configured in the cloud, and the scene to be collected is sent to the vehicle according to specific needs. When the vehicle receives the demand for the scene of interest, it identifies the scene, triggers data recording, and uploads it to cloud. For example, if it is necessary to collect autonomous driving lane changing scene data, it can be sent to the car. The car will then identify the vehicle's lane changing behavior based on the sensing data, then record the overall lane changing process data and upload it to the cloud storage application.

需要说明的是，感兴趣场景可根据感知、定位和高精地图信息综合判断：It should be noted that the scene of interest can be comprehensively judged based on perception, positioning and high-precision map information:

(21)道路类型(高速/城市/园区…)、形状(直道/弯道/掉头…)、车道数量、信号灯；(21) Road type (highway/city/park...), shape (straight/curve/U-turn...), number of lanes, and traffic lights;

(22)障碍物数量、类别、位置、速度等；(22) Number, type, location, speed, etc. of obstacles;

(23)自车当前速度、加速度、灯光状态等。(23) Current speed, acceleration, lighting status of the vehicle, etc.

在车辆位于人工驾驶模式时，图6是根据本公开实施例的数据采集触发机制图(二)，如附图3所示，数据采集触发机制分为三种情况：When the vehicle is in the manual driving mode, Figure 6 is a diagram (2) of the data collection triggering mechanism according to an embodiment of the present disclosure. As shown in Figure 3, the data collection triggering mechanism is divided into three situations:

(1)人车差异分析识别(1) Analysis and identification of differences between people and vehicles

在人工驾驶模式下，自动驾驶***模拟运行，其控制指令不直接操控车辆，此时将驾驶员的操作行为和车辆的实际状态与自动驾驶***的控制指令进行差异分析，当二者差异超过设定阈值时(当二者差异超过设定阈值时，说明车辆位于预测控制指令异常场景)，触发数据记录。In manual driving mode, the automatic driving system simulates operation, and its control instructions do not directly control the vehicle. At this time, the driver's operating behavior and the actual state of the vehicle are analyzed for differences with the control instructions of the automatic driving system. When the difference between the two exceeds the set When a set threshold is reached (when the difference between the two exceeds the set threshold, it indicates that the vehicle is in an abnormal scenario of predictive control instructions), data recording is triggered.

(2)自动驾驶异常识别(2) Automatic driving anomaly identification

在人工驾驶模式下，自动驾驶异常识别包含感知数据异常、定位数据异常和规划数据异常，这三类异常的触发方法与自动驾驶模式下是一致的。In manual driving mode, automatic driving anomaly identification includes sensing data anomalies, positioning data anomalies and planning data anomalies. The triggering methods of these three types of anomalies are consistent with those in automatic driving mode.

(3)感兴趣场景识别(3) Interesting scene recognition

在人工驾驶模式下的感兴趣场景识别与自动驾驶模式下是一致的。The identification of interesting scenes in manual driving mode is consistent with that in automatic driving mode.

需要说明的是，本公开为了实现自动驾驶***车端数据有效收集，在自动驾驶模式和人工驾驶模式下都设计触发机制进行数据采集存储。It should be noted that in order to achieve effective collection of vehicle-side data of the automatic driving system, this disclosure designs a trigger mechanism for data collection and storage in both automatic driving mode and manual driving mode.

在自动驾驶模式下，设计自动驾驶异常场景数据触发和感兴趣场景触发，提升自动驾驶模式下数据的有效采集。在人工驾驶模式下，通过自动驾驶***的虚拟运行，在采集到自动驾驶异常场景数据和感兴趣场景的基础上，增加人车差异场景触发，能够快速收集自动驾驶***与人工驾驶员的差距，有效提升车端场景的覆盖度，并对自动驾驶***优化开发提供场景数据支持。In the automatic driving mode, the automatic driving abnormal scene data trigger and the interesting scene trigger are designed to improve the effective collection of data in the automatic driving mode. In manual driving mode, through the virtual operation of the automatic driving system, based on the collection of abnormal scene data of automatic driving and scenes of interest, the difference scene trigger between humans and vehicles can be added to quickly collect the gap between the automatic driving system and the manual driver. Effectively improve the coverage of vehicle-side scenarios and provide scenario data support for the optimization and development of autonomous driving systems.

其中，为了有效收集自动驾驶异常场景数据，分五种情况分别触发数据采集，具体包含自动驾驶异常接管场景、自动驾驶控制指令异常、感知 数据异常、定位数据异常和预测规划数据异常，能够从整体和分模块对异常场景进行有效触发。Among them, in order to effectively collect abnormal scenario data of autonomous driving, data collection is triggered in five situations, including abnormal takeover scenarios of autonomous driving, abnormal autonomous driving control instructions, and perception Data anomalies, positioning data anomalies and predictive planning data anomalies can effectively trigger abnormal scenarios from the overall and sub-module levels.

为了实现特定场景数据采集，设计感兴趣场景识别模块，并设计云端场景配置功能，可在云端远程下发感兴趣场景，快速实现特定场景数据采集。In order to realize data collection of specific scenes, a scene identification module of interest is designed, and a cloud scene configuration function is designed, which can remotely deliver scenes of interest in the cloud and quickly realize data collection of specific scenes.

通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到根据上述实施例的方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本公开各个实施例的方法。Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is Better implementation. Based on this understanding, the technical solution of the present disclosure can be embodied in the form of a software product in essence or that contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of various embodiments of the present disclosure.

在本实施例中还提供了一种车辆场景数据的采集装置，该装置设置为实现上述实施例及优选实施方式，已经进行过说明的不再赘述。如以下所使用的，术语“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的设备较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。This embodiment also provides a device for collecting vehicle scene data. The device is configured to implement the above embodiments and preferred implementations. What has already been explained will not be described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

图7是根据本公开实施例的车辆场景数据的采集装置的结构框图，该装置包括：Figure 7 is a structural block diagram of a device for collecting vehicle scene data according to an embodiment of the present disclosure. The device includes:

获取模块72，设置为在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；The acquisition module 72 is configured to acquire target data determined by the automatic driving system of the vehicle while the vehicle is driving, wherein the target data includes at least one of the following: control instructions for the vehicle by the automatic driving system. , the sensing data perceived by the automatic driving system of the environment in which the vehicle is located, the positioning data of the vehicle determined by the automatic driving system, and the predictive planning data predicted by the automatic driving system;

确定模块74，设置为根据所述目标数据确定所述车辆是否处于目标场景；Determining module 74, configured to determine whether the vehicle is in a target scene according to the target data;

采集模块76，设置为在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。The collection module 76 is configured to collect vehicle scene data of the vehicle in the target scene when it is determined that the vehicle is in the target scene.

通过上述装置，在车辆行驶的过程中，通过车辆自动判断是否在设定 的目标场景下，进而触发数据采集，由于车辆识别场景的能力比人工识别的场景的能力更强，进而车辆可以识别的场景更多，并且车辆在行驶时所处的场景具有多样性，进而可以收集到大量的真实、多样的场景数据，进而解决了获取到的车辆场景数据较少的问题。Through the above device, when the vehicle is driving, the vehicle automatically determines whether the setting In the target scene, data collection is triggered. Since the vehicle's ability to recognize scenes is stronger than that of manual recognition, the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, so it can A large amount of real and diverse scene data is collected, thus solving the problem of less vehicle scene data being obtained.

在一个示例性的实施例中，确定模块，设置为在所述目标数据包括所述控制指令的情况下，通过以下方式确定所述车辆是否处于目标场景：在确定所述车辆处于自动驾驶模式、且所述控制指令指示将所述车辆在第一方向上的加速度设置为第一加速度的情况下，确定所述车辆处于行驶异常场景，其中，所述第一方向为所述车辆的前进方向，所述第一加速度的值超过第一阈值；或者在确定所述车辆处于自动驾驶模式、所述控制指令指示将所述车辆在第二方向的加速度设置为第二加速度、且指示将所述车辆的轮胎的转角变化率设置为目标转角变化率的情况下，确定所述车辆处于行驶异常场景，其中，所述第二加速度超过第二阈值，所述目标转角变化率超过第三阈值，所述第二方向与所述第二方向之间的夹角为预设夹角；在确定所述车辆处于人工驾驶模式的情况下，确定目标对象对车辆下发的目标控制指令；将所述目标控制指令与所述目标数据中的控制指令进行比对，并在所述目标控制指令与所述控制指令的相似度小于第四阈值的情况，确定所述车辆处于预测控制指令异常场景；其中，所述目标场景包括：所述行驶异常场景，所述预测控制指令异常场景。In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in a target scene in the following manner: when determining that the vehicle is in an autonomous driving mode, And when the control instruction indicates that the acceleration of the vehicle in the first direction is set to the first acceleration, it is determined that the vehicle is in an abnormal driving scene, wherein the first direction is the forward direction of the vehicle, The value of the first acceleration exceeds a first threshold; or when it is determined that the vehicle is in the automatic driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration and instructs the vehicle to When the tire's rotation angle change rate is set to the target rotation angle change rate, it is determined that the vehicle is in an abnormal driving scene, wherein the second acceleration exceeds a second threshold, the target rotation angle change rate exceeds a third threshold, and the The angle between the second direction and the second direction is a preset angle; when it is determined that the vehicle is in manual driving mode, determine the target control instruction issued by the target object to the vehicle; control the target The instruction is compared with the control instruction in the target data, and when the similarity between the target control instruction and the control instruction is less than a fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario; wherein, The target scenario includes: the driving abnormality scenario and the predictive control instruction abnormality scenario.

在一个示例性的实施例中，确定模块，设置为在所述目标数据包括所述感知数据的情况下，通过以下方式确定所述车辆是否处于目标场景：在根据所述感知数据确定所述自动驾驶***的感知模块感知到的第一障碍物的类型在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；或者在根据所述感知数据确定第一障碍物的标识在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；或者在根据所述感知数据确定第一障碍物的移动速度在第一预设时长内的变化量超过第五阈值的情况下，确定所述车辆处于感知模块异常场景；或者在根据所述感知数据确定所述感知模块感知到的第二障碍物的位置在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；在根据所述感知数据确定所述感知模块感知到的第三障碍物的位置与所述车辆的位置重叠的情况下，确定所述车辆处于感知模块异常场景；在所述 感知数据为预设感知数据，以及所述车辆的车身数据为预设车身数据的情况下，确定所述车辆处于预设场景；其中，所述目标场景包括：所述感知模块异常场景，所述预设场景。In an exemplary embodiment, the determining module is configured to determine whether the vehicle is in the target scene in the following manner: when the target data includes the sensing data, the automatic When the type of the first obstacle sensed by the sensing module of the driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; or when the first obstacle is determined based on the sensing data When the identification changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module; or when it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds In the case of the fifth threshold, it is determined that the vehicle is in an abnormal scene of the sensing module; or it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period. , it is determined that the vehicle is in a sensing module abnormality scene; when it is determined according to the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in a sensing module abnormality scene. scene; described When the sensing data is preset sensing data and the vehicle body data is preset body data, it is determined that the vehicle is in a preset scene; wherein the target scene includes: an abnormal scene of the sensing module, and the Preset scenes.

在一个示例性的实施例中，确定模块，设置为在所述目标数据包括所述定位数据的情况下，通过以下方式确定所述车辆是否处于目标场景：在根据所述定位数据确定所述车辆的位置在第二预设时长内的变化量超过第六阈值的情况下，确定所述车辆处于定位模块异常场景；或者在根据所述定位数据确定所述车辆的移动方向在所述第二预设时长内的变化量超过第七阈值的情况下，确定所述车辆处于定位模块异常场景；或者在根据所述定位数据确定所述车辆的位置在所述第二预设时长内未发生变化，但所述车辆的速度为目标速度的情况下、确定所述车辆处于定位模块异常场景；其中，所述目标场景包括：所述定位模块异常场景。In an exemplary embodiment, the determination module is configured to determine whether the vehicle is in the target scene in the following manner: when the target data includes the positioning data, the vehicle is determined according to the positioning data. If the change in the position of the vehicle exceeds the sixth threshold within the second preset time period, it is determined that the vehicle is in an abnormal scene of the positioning module; or it is determined based on the positioning data that the moving direction of the vehicle is in the second preset time period. If the amount of change within the duration exceeds the seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module; or it is determined based on the positioning data that the position of the vehicle has not changed within the second preset duration, However, when the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module; wherein the target scene includes: an abnormal scene of the positioning module.

在一个示例性的实施例中，确定模块，设置为在所述目标数据包括所述预测规划数据的情况下，通过以下方式确定所述车辆是否处于目标场景：在所述预测规划数据设置为指示第三障碍物的预测移动状态的情况下，确定所述预测移动状态与所述第三障碍物的目标移动状态的相似度，并在所述相似度小于第八阈值的情况下，确定所述车辆处于预测规划模块异常场景，其中，所述目标移动状态为对所述第三障碍物进行检测后所得到的移动状态；或者在所述预测规划数据设置为指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹的可行性，并在所述可行性小于第九阈值的情况下，确定所述车辆处于预测规划模块异常场景；或者在所述预测规划数据设置为指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹上是否具有障碍物，并在确定所述规划移动轨迹上具有障碍物的情况下，确定所述车辆处于预测规划模块异常场景；或者在所述预测规划数据设置为指示所述自动驾驶***无法控制所述车辆的情下，确定所述车辆处于待人工控制场景；其中，所述目标场景包括：所述预测规划模块异常场景，所述待人工控制场景。In an exemplary embodiment, the determination module is configured to determine whether the vehicle is in a target scenario in the following manner: when the target data includes the predictive planning data, the predictive planning data is set to indicate In the case of the predicted movement state of the third obstacle, determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine the The vehicle is in an abnormal scenario of the prediction planning module, wherein the target movement state is the movement state obtained after detecting the third obstacle; or the prediction planning data is set to indicate the planned movement trajectory of the vehicle. In this case, determine the feasibility of the planned movement trajectory, and when the feasibility is less than the ninth threshold, determine that the vehicle is in an abnormal scenario of the predictive planning module; or when the predictive planning data is set to indicate the In the case of the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine that the vehicle is in an abnormal scenario of the prediction planning module; or When the predictive planning data is set to indicate that the automatic driving system cannot control the vehicle, it is determined that the vehicle is in a scenario to be manually controlled; wherein the target scenario includes: an abnormal scenario of the predictive planning module, and the The scene is to be manually controlled.

在一个示例性的实施例中，确定模块，设置为通过以下方式采集所述车辆在所述目标场景下的车辆场景数据：采集所述车辆在预设时间段内的车身数据，其中，在所述预设时间段内，所述车辆处于所述目标场景；采集所述车辆在所述预设时间段内的底盘数据；采集所述车辆在所述预设时 间段内，所述自动驾驶***产生的过程数据以及控制指令数据；采集所述车辆在所述预设时间段内通过图像采集装置以及雷达传感器确定的所述车辆所处环境的感知数据。In an exemplary embodiment, the determination module is configured to collect vehicle scene data of the vehicle in the target scene in the following manner: collect vehicle body data of the vehicle within a preset time period, wherein, in the During the preset time period, the vehicle is in the target scene; the chassis data of the vehicle during the preset time period is collected; and the chassis data of the vehicle during the preset time period is collected. During the interval, the process data and control instruction data generated by the automatic driving system are collected; the perception data of the environment of the vehicle determined by the image acquisition device and the radar sensor within the preset time period are collected.

在一个示例性的实施例中，上述装置还包括：处理模块，设置为采集所述车辆在所述目标场景下的车辆场景数据之后，将所述车辆场景数据发送至云端服务器，以使所述云端服务器根据所述车辆场景数据调整所述自动驾驶***的算法；获取所述云端服务器调整后的目标算法，并将所述自动驾驶***的算法更新为所述目标算法。In an exemplary embodiment, the above device further includes: a processing module configured to, after collecting vehicle scene data of the vehicle in the target scene, send the vehicle scene data to the cloud server so that the The cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data; obtains the adjusted target algorithm from the cloud server, and updates the algorithm of the automatic driving system to the target algorithm.

本公开的实施例还提供了一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序，其中，该计算机程序被设置为运行时执行上述任一项方法实施例中的步骤。Embodiments of the present disclosure also provide a computer-readable storage medium that stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

可选地，在本实施例中，上述存储介质可以被设置为存储设置为执行以下步骤的计算机程序：Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program configured to perform the following steps:

S1，在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；S1. During the driving process of the vehicle, obtain the target data determined by the automatic driving system of the vehicle, wherein the target data includes at least one of the following: the control instruction of the automatic driving system to the vehicle, the automatic driving system Sensing data perceived by the driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and predictive planning data predicted by the automatic driving system;

S2，根据所述目标数据确定所述车辆是否处于目标场景；S2, determine whether the vehicle is in the target scene according to the target data;

S3，在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。S3: When it is determined that the vehicle is in the target scene, collect vehicle scene data of the vehicle in the target scene.

在一个示例性实施例中，上述计算机可读存储介质可以包括但不限于：U盘、只读存储器(Read-Only Memory，简称为ROM)、随机存取存储器(Random Access Memory，简称为RAM)、移动硬盘、磁碟或者光盘等各种可以存储计算机程序的介质。In an exemplary embodiment, the computer-readable storage medium may include but is not limited to: U disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM) , mobile hard disk, magnetic disk or optical disk and other media that can store computer programs.

本实施例中的具体示例可以参考上述实施例及示例性实施方式中所描述的示例，本实施例在此不再赘述。For specific examples in this embodiment, reference may be made to the examples described in the above-mentioned embodiments and exemplary implementations, and details will not be described again in this embodiment.

需要说明的是，本公开上述的存储介质可以是计算机可读信号介质或 者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的***、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行***、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读信号介质还可以是计算机可读存储介质以外的任何存储介质，该计算机可读信号介质可以发送、传播或者传输设置为由指令执行***、装置或者器件使用或者与其结合使用的程序。存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF(射频)等等，或者上述的任意合适的组合。It should be noted that the above-mentioned storage medium of the present disclosure may be a computer-readable signal medium or or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard drive, random access memory (RAM), read only memory (ROM), removable Programmed read-only memory (EPROM or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any storage medium other than computer-readable storage media that can transmit, propagate, or transport a program configured for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

本公开的实施例还提供了一种电子装置，包括存储器和处理器，该存储器中存储有计算机程序，该处理器被设置为运行计算机程序以执行上述任一项方法实施例中的步骤。Embodiments of the present disclosure also provide an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

可选地，在本实施例中，上述处理器可以被设置为通过计算机程序执行以下步骤：Optionally, in this embodiment, the above-mentioned processor may be configured to perform the following steps through a computer program:

S1，在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；S1. During the driving process of the vehicle, obtain the target data determined by the automatic driving system of the vehicle, wherein the target data includes at least one of the following: the control instruction of the automatic driving system to the vehicle, the automatic driving system Sensing data perceived by the driving system of the environment in which the vehicle is located, positioning data of the vehicle determined by the automatic driving system, and predictive planning data predicted by the automatic driving system;

S2，根据所述目标数据确定所述车辆是否处于目标场景；S2, determine whether the vehicle is in the target scene according to the target data;

S3，在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所 述目标场景下的车辆场景数据。S3: When it is determined that the vehicle is in the target scene, collect the location of the vehicle Vehicle scene data in the target scene.

在一个示例性实施例中，上述电子装置还可以包括传输设备以及输入输出设备，其中，该传输设备和上述处理器连接，该输入输出设备和上述处理器连接。In an exemplary embodiment, the above-mentioned electronic device may further include a transmission device and an input-output device, wherein the transmission device is connected to the above-mentioned processor, and the input-output device is connected to the above-mentioned processor.

本实施例中的具体示例可以参考上述实施例及示例性实施方式中所描述的示例，本实施例在此不再赘述。For specific examples in this embodiment, reference may be made to the examples described in the above-mentioned embodiments and exemplary implementations, and details will not be described again in this embodiment.

显然，本领域的技术人员应该明白，上述的本公开的各模块或各步骤可以用通用的计算装置来实现，它们可以集中在单个的计算装置上，或者分布在多个计算装置所组成的网络上，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，并且在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样，本公开不限制于任何特定的硬件和软件结合。Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present disclosure can be implemented using general-purpose computing devices, and they can be concentrated on a single computing device, or distributed across a network composed of multiple computing devices. They may be implemented in program code executable by a computing device, such that they may be stored in a storage device for execution by the computing device, and in some cases may be executed in a sequence different from that shown herein. Or the described steps can be implemented by making them into individual integrated circuit modules respectively, or by making multiple modules or steps among them into a single integrated circuit module. As such, the present disclosure is not limited to any specific combination of hardware and software.

以上所述为本公开的优选实施例而已，并不用于限制本公开，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。The above are only preferred embodiments of the present disclosure and are not intended to limit the present disclosure. For those skilled in the art, the present disclosure may have various modifications and changes. Any modifications, equivalent substitutions, improvements, etc. made within the principles of this disclosure shall be included in the protection scope of this disclosure.

工业实用性Industrial applicability

本公开实施例所提供的车辆场景数据的采集方法及装置可应用于车辆行驶的过程中，通过车辆自动判断是否在设定的目标场景下，进而触发数据采集，由于车辆识别场景的能力比人工识别的场景的能力更强，进而车辆可以识别的场景更多，并且车辆在行驶时所处的场景具有多样性，进而可以收集到大量的真实、多样的场景数据，进而解决了获取到的车辆场景数据较少的问题。 The vehicle scene data collection method and device provided by the embodiments of the present disclosure can be applied to the process of driving the vehicle, and the vehicle automatically determines whether it is in the set target scene, thereby triggering data collection. Since the vehicle's ability to recognize scenes is better than manual The ability to recognize scenes is stronger, and the vehicle can recognize more scenes, and the scenes in which the vehicle is driving are diverse, and a large amount of real and diverse scene data can be collected, thus solving the problem of obtaining vehicle The problem of less scene data.

Claims (10)

1. 一种车辆场景数据的采集方法，所述方法包括：A method for collecting vehicle scene data, the method includes:

   在车辆行驶的过程中，获取所述车辆的自动驾驶***确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；During the driving process of the vehicle, target data determined by the automatic driving system of the vehicle is obtained, wherein the target data includes at least one of the following: the control instructions of the automatic driving system for the vehicle, the automatic driving system Perception data of the environment in which the vehicle is located, positioning data of the vehicle determined by the autonomous driving system, and predictive planning data predicted by the autonomous driving system;

   根据所述目标数据确定所述车辆是否处于目标场景；Determine whether the vehicle is in a target scene according to the target data;

   在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。When it is determined that the vehicle is in the target scene, vehicle scene data of the vehicle in the target scene is collected.
2. 根据权利要求1所述的车辆场景数据的采集方法，其中，在所述目标数据包括所述控制指令的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：The vehicle scene data collection method according to claim 1, wherein, when the target data includes the control instruction, determining whether the vehicle is in the target scene according to the target data includes:

   在确定所述车辆处于自动驾驶模式、且所述控制指令指示将所述车辆在第一方向上的加速度设置为第一加速度的情况下，确定所述车辆处于行驶异常场景，其中，所述第一方向为所述车辆的前进方向，所述第一加速度的值超过第一阈值；When it is determined that the vehicle is in the autonomous driving mode and the control instruction indicates that the acceleration of the vehicle in the first direction is set to a first acceleration, it is determined that the vehicle is in an abnormal driving scenario, wherein the third One direction is the forward direction of the vehicle, and the value of the first acceleration exceeds a first threshold;

   在确定所述车辆处于自动驾驶模式、所述控制指令指示将所述车辆在第二方向的加速度设置为第二加速度、且指示将所述车辆的轮胎的转角变化率设置为目标转角变化率的情况下，确定所述车辆处于行驶异常场景，其中，所述第二加速度超过第二阈值，所述目标转角变化率超过第三阈值，所述第二方向与所述第二方向之间的夹角为预设夹角；After it is determined that the vehicle is in the autonomous driving mode, the control instruction instructs to set the acceleration of the vehicle in the second direction to the second acceleration, and instructs to set the rotation angle change rate of the vehicle's tires to the target rotation angle change rate. In this case, it is determined that the vehicle is in an abnormal driving scenario, wherein the second acceleration exceeds a second threshold, the target angle change rate exceeds a third threshold, and the angle between the second direction and the second direction is Angle is the preset included angle;

   在确定所述车辆处于人工驾驶模式的情况下，确定目标对象对车辆下发的目标控制指令；将所述目标控制指令与所述目标数据中的控制指令进行比对，并在所述目标控制指令与所述控制指令的相似度小于第四阈值的情况，确定所述车辆处于预测控制指令异常场景；When it is determined that the vehicle is in manual driving mode, the target control instruction issued by the target object to the vehicle is determined; the target control instruction is compared with the control instruction in the target data, and the target control instruction is If the similarity between the instruction and the control instruction is less than the fourth threshold, it is determined that the vehicle is in a predictive control instruction abnormal scenario;

   其中，所述目标场景包括：所述行驶异常场景，所述预测控制指令异常场景。Wherein, the target scene includes: the driving abnormality scene and the predictive control instruction abnormality scene.
3. 根据权利要求1所述的车辆场景数据的采集方法，其中，在所述目标数据包括所述感知数据的情况下，根据所述目标数据确定所述车辆是否 处于目标场景，包括：The vehicle scene data collection method according to claim 1, wherein when the target data includes the sensing data, it is determined based on the target data whether the vehicle Be in the target scenario, including:

   在根据所述感知数据确定所述自动驾驶***的感知模块感知到的第一障碍物的类型在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；If it is determined based on the sensing data that the type of the first obstacle sensed by the sensing module of the autonomous driving system changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

   在根据所述感知数据确定第一障碍物的标识在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；In the case where it is determined based on the sensing data that the identification of the first obstacle changes within the first preset time period, it is determined that the vehicle is in a sensing module abnormal scene;

   在根据所述感知数据确定第一障碍物的移动速度在第一预设时长内的变化量超过第五阈值的情况下，确定所述车辆处于感知模块异常场景；If it is determined based on the sensing data that the change in the moving speed of the first obstacle within the first preset time period exceeds the fifth threshold, it is determined that the vehicle is in a sensing module abnormal scene;

   在根据所述感知数据确定所述感知模块感知到的第二障碍物的位置在第一预设时长内发生变化的情况下，确定所述车辆处于感知模块异常场景；If it is determined based on the sensing data that the position of the second obstacle sensed by the sensing module changes within the first preset time period, it is determined that the vehicle is in an abnormal scene of the sensing module;

   在根据所述感知数据确定所述感知模块感知到的第三障碍物的位置与所述车辆的位置重叠的情况下，确定所述车辆处于感知模块异常场景；When it is determined based on the sensing data that the position of the third obstacle sensed by the sensing module overlaps with the position of the vehicle, it is determined that the vehicle is in an abnormal scene of the sensing module;

   在所述感知数据为预设感知数据，以及所述车辆的车身数据为预设车身数据的情况下，确定所述车辆处于预设场景；In the case where the sensing data is preset sensing data and the vehicle body data is preset body data, it is determined that the vehicle is in a preset scene;

   其中，所述目标场景包括：所述感知模块异常场景，所述预设场景。Wherein, the target scene includes: the abnormal scene of the sensing module and the preset scene.
4. 根据权利要求1所述的车辆场景数据的采集方法，其中，在所述目标数据包括所述定位数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：The vehicle scene data collection method according to claim 1, wherein, when the target data includes the positioning data, determining whether the vehicle is in the target scene according to the target data includes:

   在根据所述定位数据确定所述车辆的位置在第二预设时长内的变化量超过第六阈值的情况下，确定所述车辆处于定位模块异常场景；In the case where it is determined based on the positioning data that the change in the position of the vehicle within the second preset time period exceeds a sixth threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

   在根据所述定位数据确定所述车辆的移动方向在所述第二预设时长内的变化量超过第七阈值的情况下，确定所述车辆处于定位模块异常场景；When it is determined based on the positioning data that the change in the movement direction of the vehicle within the second preset time period exceeds a seventh threshold, it is determined that the vehicle is in an abnormal scene of the positioning module;

   在根据所述定位数据确定所述车辆的位置在所述第二预设时长内未发生变化，但所述车辆的速度为目标速度的情况下、确定所述车辆处于定位模块异常场景；When it is determined based on the positioning data that the position of the vehicle has not changed within the second preset time period but the speed of the vehicle is the target speed, it is determined that the vehicle is in an abnormal scene of the positioning module;

   其中，所述目标场景包括：所述定位模块异常场景。Wherein, the target scenario includes: the abnormal scenario of the positioning module.
5. 根据权利要求1所述的车辆场景数据的采集方法，其中，在所述目标数据包括所述预测规划数据的情况下，根据所述目标数据确定所述车辆是否处于目标场景，包括：The vehicle scene data collection method according to claim 1, wherein, when the target data includes the prediction planning data, determining whether the vehicle is in the target scene according to the target data includes:

   在所述预测规划数据用于指示第三障碍物的预测移动状态的情况下， 确定所述预测移动状态与所述第三障碍物的目标移动状态的相似度，并在所述相似度小于第八阈值的情况下，确定所述车辆处于预测规划模块异常场景，其中，所述目标移动状态为对所述第三障碍物进行检测后所得到的移动状态；In the case where the predictive planning data is used to indicate the predicted movement state of the third obstacle, Determine the similarity between the predicted movement state and the target movement state of the third obstacle, and when the similarity is less than an eighth threshold, determine that the vehicle is in a prediction planning module abnormal scenario, wherein, The target movement state is the movement state obtained after detecting the third obstacle;

   在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹的可行性，并在所述可行性小于第九阈值的情况下，确定所述车辆处于预测规划模块异常场景；When the predicted planning data is used to indicate the planned movement trajectory of the vehicle, the feasibility of the planned movement trajectory is determined, and when the feasibility is less than a ninth threshold, it is determined that the vehicle is in the predicted movement trajectory. Planning module abnormal scenarios;

   在所述预测规划数据用于指示所述车辆的规划移动轨迹的情况下，确定所述规划移动轨迹上是否具有障碍物，并在确定所述规划移动轨迹上具有障碍物的情况下，确定所述车辆处于预测规划模块异常场景；In the case where the predicted planning data is used to indicate the planned movement trajectory of the vehicle, determine whether there are obstacles on the planned movement trajectory, and if it is determined that there are obstacles on the planned movement trajectory, determine whether there are obstacles on the planned movement trajectory. The vehicle described above is in an abnormal scenario in the predictive planning module;

   在所述预测规划数据用于指示所述自动驾驶***无法控制所述车辆的情下，确定所述车辆处于待人工控制场景；In the case where the predictive planning data is used to indicate that the automatic driving system is unable to control the vehicle, it is determined that the vehicle is in a scenario to be manually controlled;

   其中，所述目标场景包括：所述预测规划模块异常场景，所述待人工控制场景。Wherein, the target scenario includes: the abnormal scenario of the prediction planning module and the scenario to be manually controlled.
6. 根据权利要求1所述的车辆场景数据的采集方法，其中，采集所述车辆在所述目标场景下的车辆场景数据，包括：The vehicle scene data collection method according to claim 1, wherein collecting the vehicle scene data of the vehicle in the target scene includes:

   采集所述车辆在预设时间段内的车身数据，其中，在所述预设时间段内，所述车辆处于所述目标场景；Collect body data of the vehicle within a preset time period, wherein the vehicle is in the target scene during the preset time period;

   采集所述车辆在所述预设时间段内的底盘数据；Collect chassis data of the vehicle within the preset time period;

   采集所述车辆在所述预设时间段内，所述自动驾驶***产生的过程数据以及控制指令数据；Collect the process data and control instruction data generated by the automatic driving system of the vehicle within the preset time period;

   采集所述车辆在所述预设时间段内通过图像采集装置以及雷达传感器确定的所述车辆所处环境的感知数据。Collect the sensing data of the environment in which the vehicle is located, determined by the image acquisition device and the radar sensor within the preset time period.
7. 根据权利要求1所述的车辆场景数据的采集方法，其中，采集所述车辆在所述目标场景下的车辆场景数据之后，所述方法还包括：The vehicle scene data collection method according to claim 1, wherein after collecting the vehicle scene data of the vehicle in the target scene, the method further includes:

   将所述车辆场景数据发送至云端服务器，以使所述云端服务器根据所述车辆场景数据调整所述自动驾驶***的算法；Send the vehicle scene data to a cloud server so that the cloud server adjusts the algorithm of the automatic driving system according to the vehicle scene data;

   获取所述云端服务器调整后的目标算法，并将所述自动驾驶***的算法更新为所述目标算法。Obtain the adjusted target algorithm from the cloud server, and update the algorithm of the automatic driving system to the target algorithm.
8. 一种车辆场景数据的采集装置，其中，包括：A device for collecting vehicle scene data, which includes:

   获取模块，设置为在车辆行驶的过程中，获取所述车辆的自动驾驶系 统确定的目标数据，其中，所述目标数据包括以下至少之一：所述自动驾驶***对所述车辆的控制指令、所述自动驾驶***对所述车辆所处环境感知到的感知数据、所述自动驾驶***确定的所述车辆的定位数据、所述自动驾驶***预测的预测规划数据；The acquisition module is configured to acquire the automatic driving system of the vehicle while the vehicle is driving. The target data determined by the system, wherein the target data includes at least one of the following: the control instructions of the automatic driving system to the vehicle, the sensing data of the environment in which the vehicle is located, the sensing data of the automatic driving system, the The positioning data of the vehicle determined by the automatic driving system and the predictive planning data predicted by the automatic driving system;

   确定模块，设置为根据所述目标数据确定所述车辆是否处于目标场景；a determination module configured to determine whether the vehicle is in a target scene according to the target data;

   采集模块，设置为在确定所述车辆处于所述目标场景的情况下，采集所述车辆在所述目标场景下的车辆场景数据。The collection module is configured to collect vehicle scene data of the vehicle in the target scene when it is determined that the vehicle is in the target scene.
9. 一种计算机可读存储介质，其中，所述计算机可读存储介质包括存储的程序，其中，在所述程序运行时控制所述计算机可读存储介质所在设备执行权利要求1至7中任意一项所述的车辆场景数据的采集方法。A computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of claims 1 to 7 The vehicle scene data collection method described.
10. 一种电子设备，其中所述电子设备包括一个或多个处理器；存储装置，设置为存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器实现设置为运行程序，其中，所述程序被设置为运行时执行所述权利要求1至7任一项中所述的车辆场景数据的采集方法。 An electronic device, wherein the electronic device includes one or more processors; a storage device configured to store one or more programs, and when the one or more programs are executed by the one or more processors, The one or more processors are configured to run a program, wherein the program is configured to execute the vehicle scene data collection method described in any one of claims 1 to 7 when running.