CN112105537A - 驾驶风险计算装置和方法 - Google Patents
驾驶风险计算装置和方法 Download PDFInfo
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- CN112105537A CN112105537A CN201980029308.XA CN201980029308A CN112105537A CN 112105537 A CN112105537 A CN 112105537A CN 201980029308 A CN201980029308 A CN 201980029308A CN 112105537 A CN112105537 A CN 112105537A
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Abstract
根据一个实施例,提供了一种用于评估驾驶风险的计算装置和方法。该计算装置包括输入电路和处理器。输入电路被配置为从车辆接收数据。数据包括车辆外部或车厢内部的视野的GPS数据、加速度数据或图像数据中的至少一个。之后,处理器被配置为基于从车辆接收的数据来识别多项风险、确定分配给多项风险的多个权重、并且基于针对多项风险的多个权重来生成评分。
Description
技术领域
本发明涉及一种用于评估驾驶风险的计算装置和方法,并且更具体地涉及一种用于驾驶行为分析、驾驶员风险构型(profiling)和事故预防的计算装置和方法。
背景技术
通过分析GPS、加速度计和G力数据中的模式来评估驾驶风险有助于提高驾驶安全性。如今,车辆信息的收集和传输主要是通过远程信息处理装置来完成的,并且分析的数据被汽车保险提供商广泛地采用,以评价驾驶员的行为并重现事故。在未来的半自动或自动驾驶车辆中,来自远程信息处理的分析数据也能够用于提高自动驾驶能力。
利用从GPS和三轴加速度计收集的数据,当前保险远程信息处理装置能够进行速度监视、运动检测和事件检测。然而,尽管成功地检测到某些车辆运动(例如,紧急制动、突然转弯),但是在没有情景化的情况下,可能不足以确定在发生事故的情况下是谁发生了过错。
当使用远程信息处理数据来评估驾驶实践时,存在类似的不足,主要是因为GPS和三轴加速度计无法检测驾驶员在车厢内的运动,诸如发出信号、盲点检查或系上安全带。对于保险提供商而言,这些驾驶实践的信息可能对定价不同驾驶员的保费很有价值。
因此,需要一种能够通过集成来自GPS、加速度计、车辆周围环境的相机视频和车厢内相机视频的数据来评估驾驶风险的装置和方法。通过对从车辆接收的数据进行综合分析和计算,可以生成用于驾驶的评分,并且该评分可以用于驾驶实践评估和事故预防目的。
发明内容
根据第一方面,提供了一种用于评估驾驶风险的计算装置。该计算装置包括输入电路,该输入电路被配置为从车辆接收数据,所述数据包括GPS数据、加速度数据或图像数据中的至少一个。所述计算装置还包括处理器,并且该处理器被配置为使用机器学习方法基于从车辆接收的数据来训练情况分类模型,以对各种驾驶情况进行分类。处理器还被配置为:基于从车辆接收的数据以及由情况分类模型分类的各种驾驶情况中的一种或多种来识别多项风险;确定多个权重,其中为多项风险中的每一项风险分配相应的权重;并且基于针对所述多项风险的多个权重来生成评分。
根据第二方面,提供了一种用于评估驾驶风险的方法。该方法包括:从车辆接收数据,所述数据包括GPS数据、加速度数据和图像数据中的至少一个;使用机器学习方法基于从车辆接收的数据来训练情况分类模型,以对各种驾驶情况进行分类;之后,基于从车辆接收的数据以及由情况分类模型分类的各种驾驶情况中的一种或多种,来识别多项风险;确定多个权重,其中为多项风险中的每一项风险分配相应的权重;并且基于针对多项风险的多个权重来生成评分。
附图说明
附图用于示出各种实施例并且用于解释根据本实施例的各种原理和优点,在附图中,相同的附图标记在各个单独的视图中指代相同的元件或功能类似的元件,并且这些附图与下面的详细描述一起并入说明书中并构成说明书的一部分。
图1示出了根据本实施例的用于评估驾驶风险的计算装置的图示。
图2示出了根据本实施例的用于评估车辆的驾驶风险的***的图示。
图3示出了根据本实施例的情况分类模型的框图。
图4示出了根据本实施例的操纵分类模型(manoeuver classification model)的框图。
图5示出了根据本实施例的评估驾驶风险所涉及的步骤的流程图。
本领域技术人员将理解,图中的元件是为了简单和清楚而示出的,因此不一定按比例示出。例如,相对于其他元件,可以放大图示或图中的一些元件的尺寸,以帮助提高对本实施例的理解。
具体实施方式
将参照附图仅通过示例的方式描述本发明的实施例。附图中相同的附图标记和字符表示相同的元件或等同物。
下面的描述的一些部分在对计算机存储器内的数据的操作的算法和功能或符号表示方面明确地或隐含地呈现。这些算法描述和功能或符号表示是数据处理领域的技术人员用来最有效地向本领域其他技术人员传达其工作实质的手段。算法在这里通常被认为是导致所需结果的步骤的自洽序列。这些步骤是需要对物理量(诸如能够被存储、传输、组合、比较和以其他方式操纵的电、磁或光信号)进行物理操纵的步骤。
除非另有明确说明并且如从以下内容显而易见,将理解的是,在整个当前说明书中,利用诸如“确定”、“计算”、“生成”、“处理”、“接收”、“收集”、“存储”等的术语的论述是指计算机***或类似电子装置的动作和过程,该动作和过程将表示为计算机***内的物理量的数据操纵和转换为类似地表示为计算机***或其他信息存储、传输或显示装置内的物理量的其他数据。
参照图1,示出了根据本实施例的用于评估驾驶风险的计算装置10的图示100。在本实施例中,计算装置10可以是具有执行编程指令的能力的各种类型,其包括输入电路12和处理器14。尽管为了清楚起见示出了单个输入电路和单个处理器,但是计算装置10还可以包括多个输入电路和多处理器***。
输入电路12被配置为从车辆接收数据120。数据120可以包括GPS数据122,GPS数据122可以提供车辆的位置、车辆的速度、行驶距离和行驶方向的信息。数据120还可以包括加速度数据124,该加速度数据124可以从车辆的加速度计(例如三轴加速度计)获得。加速度数据124可以包括在不同方向或平面中的线性或非线性的加速度。数据120可以进一步包括图像数据126。图像数据126可以从放置在车辆的各个位置中的一个或多个静物相机或视频相机获得。图像数据126可以由具有车辆外部周围环境的外部视野的相机来捕获,外部环境包括道路状况、交通状况、盲区状况、天气状况、照明状况和其他车辆。另外,图像数据126还包括由具有车辆内部视野(例如,车厢内视野)的相机捕获的图像。对于需要驾驶员的非自动驾驶车辆,具有车辆内部视野的相机可以提供驾驶员的姿势和运动(例如头部运动、手部运动或眼睛运动)的图像。
参照图2,示出了根据本实施例的用于评估车辆的驾驶风险的***的图示。车辆中用于收集数据120的装置可以包括GPS、三轴加速度计、至少一个具有车辆外部周围环境的外部视野的相机、以及至少一个具有车厢内视野的相机。在本实施例中,数据120由服务器20进一步处理。如图示的实施例中所示,服务器20与车辆分开定位并且可以经由云远程访问。替代地,服务器20可以位于车辆内部,在此处收集GPS数据122、加速度数据124和图像数据126。优选地,服务器20连接到通信总线以接收数据120,这允许处理从车辆收集的实时数据。替代地,数据120可以存储在各种形式的存储器或存储介质(例如,随机存取存储器、只读存储器、硬盘驱动器、可移动存储驱动器)中,并随后在服务器20处进行处理。
参照图3,示出了根据本实施例的情况分类模型的框图的图示300。从车辆接收的数据被组合并处理以训练情况分类模型。优选地,使用不同的机器学习方法来有效地训练情况分类模型。例如,使用可以是传统计算机视觉方法或深度学习方法(例如卷积神经网络)的计算机视觉方法来处理图像数据。优选地,来自具有外部视野的相机的图像数据的计算机视觉分析可以提供关于交通状况(例如,慢速行驶交通)、道路状况(例如,交叉路口、斑马线、圆丘(hump)、斜坡)、天气状况(例如,雨、雪)和其他车辆检测(例如,前方车辆驾驶、车辆切换车道)的信息。非图像数据可以通过其他机器学习方法进行处理,其他机器学习方法诸如为随机森林、支持向量机、线性回归、逻辑回归、最邻近算法和决策树。
从车辆收集的其他数据(例如GPS位置、车辆速度以及根据加速度数据和G力数据的操纵/碰撞检测)也用于情况分类模型。从车辆收集的数据可以进一步包括来自附加传感器或装置的数据以提供更全面的数据集,诸如温度数据、湿度数据、轮胎压力等。
基于以上呈现的所有数据,可以训练情况分类模型以对各种驾驶情况进行分类,包括但不限于以下的一种或多种:接近交通信号灯管制的路口;接近交通标志管制的路口;接近非管制交叉路口;接近非管制T路口;接近斑马线;从主要/次要道路转向主要/次要道路;掉头;倒车;慢速/快速行驶交通中的车道变更;在慢速/快速行驶交通中超车;在单车道上超车;越过急弯;驾驶上坡/下坡;停止上坡/下坡;移离下坡/下坡;避免对车辆的前部或侧面造成危害;进/出高速公路;下雨/下雪天气;以及能见度高/低。
参照图4,示出了根据本实施例的操纵分类模型的框图的图示400。从车辆接收的数据被组合并处理以训练操纵分类模型。优选地,使用不同的机器学习方法来有效地训练操纵分类模型。例如,使用可以是传统计算机视觉方法或深度学习方法(例如卷积神经网络)的计算机视觉方法来处理图像数据。优选地,对于需要驾驶员的非自动驾驶车辆,来自具有车厢内视野的相机的计算机视觉分析可以提供关于驾驶员头部、手部和眼睛运动的信息(例如,检查盲点、戴上安全带、发出信号)。
从车辆收集的其他数据(例如GPS位置、车辆速度以及根据加速度数据和G力数据的操纵/碰撞检测)也用于操纵分类模型。基于上面呈现的所有数据,可以对操纵分类模型进行训练,以对各个驾驶员和车辆操纵进行分类,包括但不限于以下的一项或多项:加速;制动;转向;发出信号;接合/释放手刹;检查后视镜;检查侧视镜;检查盲点;戴上/解下安全带;以及在分神/陶醉/昏昏欲睡/困倦时驾驶。
并且参照图5,示出了根据本实施例的用于评估驾驶风险的方法中所涉及的步骤的流程图500。在步骤502,从车辆收集数据并由计算装置接收数据。来自车辆的数据包括以下至少之一:GPS数据、加速度数据和图像数据。也可以包括被认为与评估驾驶风险有关的来自车辆的其他数据,例如温度数据或来自车辆的附加传感器的数据。
在步骤504,基于从车辆接收的数据来识别多项风险。多项风险可能包括但不限于以下的一项或多项:未保持安全距离;未遵守交通信号灯或交通标志;未在路口处减速;未发出信号;未进行安全检查;以及鲁莽驾驶。由计算装置基于先前从车辆收集的数据来进行识别风险。更具体地,计算装置将情况分类模型和操纵分类模型的输出进行组合,并且基于组合输出的匹配来识别多项风险。可选地,在步骤504处可以具有集成模型,用于处理来自情况分类模型和操纵分类模型的输出。
本公开的识别风险的方法的优点在于,归因于情况分类模型,其对较复杂的驾驶场景进行分类并且更准确地识别驾驶风险。例如,如果情况分类模型显示驾驶员在快速行驶交通中在高速公路上行驶,并且驾驶员的车辆不靠近前方车辆而后方具有车辆,则在这种情况下的驾驶员紧急制动将被识别为风险。相反,如果情况分类模型显示前方车辆突然紧急制动,则在这种情况下,在保持与前方车辆的安全距离的情况下的驾驶员紧急制动将不被识别为风险。
在另一个示例中,如果情况分类模型显示车辆正在接近交通信号灯路口并且交通信号灯为红色,则驾驶员加速将被识别为不遵守交通信号灯的风险。在另一种情况下,如果交通信号灯为黄色,则可以将驾驶员加速识别为鲁莽驾驶的风险。
本公开的识别风险的方法的优点还在于,归因于操纵分类模型,其结合了驾驶员的操纵并更有效地识别潜在驾驶风险。例如,如果来自具有外部视野的相机的所有GPS数据、加速度数据和图像数据都显示驾驶员在道路上安全驾驶,但是具有车厢内视野的相机则显示驾驶员没有系安全带,则其将被识别为不进行安全检查的风险。其他示例包括具有车厢内视野的相机显示驾驶员频繁检查其电话、驾驶员的眼睛不在道路上、驾驶员表现出醉酒/疲劳/分心的迹象、驾驶员的姿势对驾驶不安全、以及驾驶员在转向或切换车道时未发出信号。
所识别的各种风险的每次发生都将被记录并保存,以便以后生成驾驶风险评分。
在步骤506,确定多个权重,以为每项风险分配相应的权重。权重是用户自定义的,并且可以基于风险的严重性或其他因素取决于用户的兴趣来确定。本公开中的计算装置和方法的用户可以是保险提供商、警察局、自动驾驶汽车公司、驾驶员本人或驾驶员的家庭成员/监护人。
参见先前的示例,不遵守交通信号灯的风险(在交通信号灯为红色时加速)可能被分配为5的权重,并且鲁莽驾驶的风险(在交通信号灯为黄色时加速)可能被分配为3的权重。对于相同类型的风险,可以确定并分配权重以反映由用户定义的严重性差异。例如,速度超过限制速度的15%可能被分配为2的权重,速度超过限制速度的30%可能被分配为7的权重,并且在车辆中有小孩时速度超过限制速度的30%可能分配为10的权重。
权重也可以由用户确定以包括更全面的因素。例如,权重可以包括惩罚***,以对连续做出相同风险的驾驶员进行处罚。例如,在切换车道时不发出信号的风险可能被分配为1的权重。如果驾驶员在一周内或在用户设置的任何时间段内做出相同的风险,则在切换车道时不发出信号的相同风险可能被分配为2的增加权重。如果随着时间的推移不断重复风险,则权重可以是用户自定义的以以更高的比率增加;或者如果驾驶员在一段时间内停止做出风险,则权重将返回到初始值(即,以奖励纠正其错误的驾驶员)。
在步骤508,基于风险的相应权重来生成评分。可以通过将所有已识别风险的权重相加或通过由用户定义的其他计算手段来计算评分。在本示例中,评分越高表示驾驶风险越高。
评分可以与某个驾驶员有关(例如,为了保险提供商的使用)。为了将发生的风险分配给正确的驾驶员,可以在具有车厢内视野的相机的图像数据上采用可用的面部识别方法以识别驾驶员。替代地,可以利用其他生物测定方法(例如,指纹匹配、虹膜匹配)或身份证明文件(例如,驾驶执照、ID、护照、智能卡)代替面部识别。
可以基于预定时间段内的先前收集的数据来生成评分,或者可以针对从车辆接收的实时数据来生成评分。数据可以由位于车辆中的计算装置10在车上进行本地处理,或者在云中进行远程处理。由于数据的广泛性,因此除了驾驶风险之外,用户还可以通过适当的分析方法获得广泛的信息。例如,基于数据的研究可以提供有关驾驶员的响应时间、一天中最安全的驾驶时间、高事故风险发生区域等的信息。此类信息可以用于驾驶训练(针对驾驶员和自动驾驶车辆)、事故预防和车载智能驾驶***的设计目的。
应当进一步理解,示例性实施例仅仅作为示例,并且不旨在以任何方式限制本发明的范围、适用性、操作或配置。而是,前面的详细描述将为本领域技术人员提供用于实施本发明的示例性实施例的便利指南(road map),应理解,可以在不脱离所附权利要求书所阐述的本发明的范围的情况下,对示例性实施例中描述的元件的各种功能和布置以及操作方法进行改变。
Claims (26)
1.一种用于评估驾驶风险的计算装置,该计算装置包括:
输入电路,该输入电路被配置为从车辆接收数据,所述数据包括GPS数据、加速度数据或图像数据中的至少一个;和
处理器,该处理器被配置为:
使用机器学习方法基于从所述车辆接收的所述数据来训练情况分类模型,以对各种驾驶情况进行分类,
其中,所述处理器还被配置为:
基于从所述车辆接收的所述数据以及由所述情况分类模型分类的各种驾驶情况中的一种或多种来识别多项风险;
确定多个权重,其中为所述多项风险中的每一项分配相应的权重;和
基于针对所述多项风险的所述多个权重来生成评分。
2.根据权利要求1所述的计算装置,其中,所述图像数据包括道路状况的图像、交通状况的图像、天气状况的图像、照明状况的图像或其他车辆的图像中的至少一个。
3.根据权利要求1所述的计算装置,其中,所述图像数据包括车辆内部的驾驶员姿势的图像或者车辆内部的驾驶员运动的图像中的至少一个。
4.根据权利要求3所述的计算装置,其中,所述处理器还被配置为使用面部识别基于所述图像数据来识别所述车辆的驾驶员。
5.根据权利要求1所述的计算装置,其中,所述处理器还被配置为使用驾驶员的生物特征数据来识别所述车辆的驾驶员。
6.根据权利要求1所述的计算装置,其中,所述处理器还被配置为使用驾驶员的身份证明文件或身份证明数据来识别所述车辆的驾驶员。
7.根据权利要求1所述的计算装置,其中,所述多项风险包括以下至少之一:未保持安全距离;未遵守交通信号灯或交通标志;未在路口处减速;未发出信号;未进行安全检查;或者鲁莽驾驶。
8.根据权利要求1所述的计算装置,其中,基于从所述车辆接收的所述数据来识别所述多项风险是基于使用机器学习方法对从所述车辆接收的先前数据的训练来进行的。
9.根据权利要求8所述的计算装置,其中,机器学习方法包括深度学习、随机森林、支持向量机、线性回归、逻辑回归、最邻近算法和决策树。
10.根据权利要求1所述的计算装置,其中,所述评分用于以下中的至少一项:评估驾驶员的行为;向保险提供商提供分析数据;或者预测半自动或自动驾驶车辆的事故。
11.根据权利要求1所述的计算装置,其中,所述处理器被配置为基于总和来生成所述评分,其中,所述总和是基于为所述多项风险中的每一项风险分配的相应权重。
12.根据权利要求11所述的计算装置,其中,所述总和是基于所述多项风险中的每一项风险出现的次数以及为所述多项风险中的每一项风险分配的相应权重。
13.根据权利要求12所述的计算装置,其中,为所述多项风险中的每一项风险分配的相应权重以预定比率增加,其中,所述预定比率基于在预定时间段内相应风险出现的次数。
14.一种用于评估驾驶风险的方法,该方法包括:
从车辆接收数据,所述数据包括GPS数据、加速度数据和图像数据中的至少一个;
使用机器学习方法基于从所述车辆接收的所述数据来训练情况分类模型,以对各种驾驶情况进行分类;
之后,基于从所述车辆接收的所述数据以及由所述情况分类模型分类的各种驾驶情况中的一种或多种来识别多项风险;
确定多个权重,其中为所述多项风险中的每一项风险分配相应的权重;和
基于针对所述多项风险的所述多个权重来生成评分。
15.根据权利要求14所述的方法,其中,所述图像数据包括道路状况的图像、交通状况的图像、天气状况的图像、照明状况的图像或其他车辆的图像中的至少一个。
16.根据权利要求14所述的方法,其中,所述图像数据包括车辆内部的驾驶员姿势的图像或者车辆内部的驾驶员运动的图像中的至少一个。
17.根据权利要求16所述的方法,其中,所述方法还包括使用面部识别基于所述图像数据来识别所述车辆的驾驶员。
18.根据权利要求14所述的方法,其中,所述方法还包括使用驾驶员的生物特征数据来识别所述车辆的驾驶员。
19.根据权利要求14所述的方法,其中,所述方法还包括使用驾驶员的身份证明文件或身份证明数据来识别所述车辆的驾驶员。
20.根据权利要求14所述的方法,其中,所述多项风险包括以下至少之一:未保持安全距离;未遵守交通信号灯或交通标志;未在路口处减速;未发出信号;未进行安全检查;或者鲁莽驾驶。
21.根据权利要求14所述的方法,其中,基于从所述车辆接收的数据来识别所述多项风险是基于使用机器学习方法对从所述车辆接收的先前数据的训练来进行的。
22.根据权利要求21所述的方法,其中,机器学习方法包括深度学习、随机森林、支持向量机、线性回归、逻辑回归、最邻近算法和决策树。
23.根据权利要求14所述的方法,其中,所述评分用于以下中的至少一项:评估驾驶员的行为;向保险提供商提供分析数据;或者预测半自动或自动驾驶车辆的事故。
24.根据权利要求14所述的方法,其中,所述方法包括基于总和来生成所述评分,其中,所述总和是基于为所述多项风险中的每一项风险分配的相应权重。
25.根据权利要求24所述的方法,其中,所述总和是基于所述多项风险中的每一项风险出现的次数以及为所述多项风险中的每一项风险分配的相应权重。
26.根据权利要求25所述的方法,其中,为所述多项风险中的每一项风险分配的相应权重以预定比率增加,其中,所述预定比率基于在预定时间段内相应风险出现的次数。
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