CN118306389A - Rear-end collision prevention control method and device, electronic equipment and storage medium - Google Patents

Abstract

The embodiment of the application discloses a rear-end collision prevention control method, device, equipment and storage medium. The method comprises the following steps: acquiring a rear vehicle image of a vehicle acquired according to a preset frequency; determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image; if the risk coefficient reaches more than a preset risk coefficient threshold value, determining the risk level of the vehicle based on the risk coefficient; and acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy. According to the embodiment of the application, the corresponding avoidance strategy can be generated according to the dangerous grade and the running state of the current vehicle, so that the vehicle is controlled to execute the avoidance strategy, rear-end collision of the rear vehicle is prevented, and the driving safety is improved.

Description

Rear-end collision prevention control method and device, electronic equipment and storage medium

Technical Field

The application relates to the technical field of intelligent driving of vehicles, in particular to a rear-end collision prevention control method and device, electronic equipment and a computer readable storage medium.

Background

With the development of technology, automobile safety control technology has become one of the important means for guaranteeing road traffic safety. The accurate estimation of the running risk of the vehicle is a key of a real-time traffic safety information prompt system, wherein in the evaluation of the following risk, the existing rear-end collision prevention active safety control method mainly detects the distance and the relative speed between the rear vehicle and the vehicle in a radar or laser ranging mode, judges whether the rear-end collision risk exists according to the information, and takes corresponding avoidance measures. In addition, there are methods for predicting the braking behavior of a preceding vehicle by recognizing the brake lights of the preceding vehicle so as to respond in advance.

The existing rear-end collision prevention active safety control method mainly detects the distance and the relative speed between a rear vehicle and the vehicle by a radar or laser ranging mode, but the radar or laser ranging mode increases the production cost of the vehicle, and the matched radar is arranged at the front end of the vehicle, so that only the braking action of the front vehicle can be identified, the running state of the rear vehicle cannot be identified, and the early warning and avoiding effects of the vehicle are limited to a certain extent.

Disclosure of Invention

To solve the above technical problems, embodiments of the present application provide a rear-end collision prevention control method and apparatus, an electronic device, and a computer readable storage medium.

According to an aspect of the embodiment of the present application, there is provided a rear-end collision prevention control method, including: acquiring a rear vehicle image of a vehicle acquired according to a preset frequency; determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image; if the risk coefficient reaches more than a preset risk coefficient threshold value, determining the risk level of the vehicle based on the risk coefficient; and acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy.

According to an aspect of the embodiment of the present application, the determining the risk coefficient corresponding to the rear vehicle based on the rear vehicle image includes: determining a vehicle type and a driving state corresponding to the rear vehicle based on the rear vehicle image; and determining a corresponding risk coefficient of the rear vehicle based on the vehicle type and the driving state.

According to an aspect of the embodiment of the present application, the method further includes: acquiring a rear vehicle image set acquired in a preset period; and calculating the corresponding running state of the rear vehicle based on the adjacent images in the rear vehicle image set.

According to an aspect of the embodiment of the present application, the calculating the driving state corresponding to the rear vehicle based on the adjacent images in the rear vehicle image set includes: calculating the vehicle speed, the acceleration and the distance between the vehicle and the vehicle corresponding to the current moment of the rear vehicle based on the adjacent images; and determining a running state corresponding to the rear vehicle based on the vehicle speed, the acceleration and the distance between the rear vehicle and the vehicle.

According to an aspect of the embodiment of the present application, the driving state further includes road information including a road type and a geometric feature of a road; the method further comprises the steps of: weighting calculation is carried out on the road type, the geometric feature of the road, the vehicle speed and acceleration corresponding to the current moment of the rear vehicle and the distance between the rear vehicle and the vehicle to obtain a calculation result, and a dynamic risk factor of the rear vehicle is determined according to the calculation result; determining a static risk factor of the rear vehicle based on the vehicle type corresponding to the rear vehicle; a risk factor for the vehicle is determined based on the static risk factor and the dynamic risk factor.

According to an aspect of the embodiment of the present application, the controlling the vehicle to execute the avoidance strategy based on the risk level of the vehicle and the driving state of the vehicle includes: if the dangerous level does not reach the preset dangerous level, generating prompt information based on the running state of the vehicle; and if the dangerous level reaches above a preset dangerous level, generating an avoidance strategy based on the running state of the vehicle, and controlling the vehicle to execute the avoidance strategy.

According to an aspect of the embodiment of the present application, the generating an avoidance strategy based on a driving state of the vehicle includes: determining avoidance parameters of the vehicle based on the running state of the vehicle and the risk coefficient, wherein the avoidance parameters comprise steering wheel rotation angle and acceleration; and generating the avoidance strategy based on the steering wheel angle and the acceleration.

According to an aspect of the embodiment of the present application, there is provided a rear-end collision prevention control apparatus including: the acquisition module is used for acquiring rear vehicle images acquired according to a preset frequency; the first determining module is used for determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image; the second determining module is used for determining the risk level of the vehicle based on the risk coefficient if the risk coefficient reaches above a preset risk coefficient threshold value; the control module is used for acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy.

According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the electronic equipment realizes the rear-end collision prevention control method.

According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions, which when executed by a processor of a computer, cause the computer to perform the rear-end collision prevention control method as described above.

According to the technical scheme provided by the embodiment of the application, the corresponding danger coefficient of the rear vehicle is determined according to the rear vehicle image acquired at the preset frequency, and when the danger coefficient reaches above the preset danger coefficient threshold value, the rear vehicle information can be acquired through the simple image acquisition equipment, so that the cost for acquiring the rear vehicle information is saved, the danger level of the vehicle can be determined according to the danger coefficient of the rear vehicle, and the corresponding avoidance strategy is generated according to the danger level and the running state of the current vehicle, so that the vehicle is controlled to execute the avoidance strategy, rear-end collision of the rear vehicle is prevented, and the driving safety is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:

FIG. 1 is a schematic view showing an implementation environment of rear-end collision prevention control during running of a vehicle according to an exemplary embodiment of the present application;

FIG. 2 is a flow chart of a rear-end collision prevention control method shown in an exemplary embodiment of the application;

FIG. 3 is a flowchart of a rear-end collision prevention control method according to another exemplary embodiment of the present application;

FIG. 4 is a flowchart of a rear-end collision prevention control method according to another exemplary embodiment of the present application;

FIG. 5 is a flowchart of a rear-end collision prevention control method according to another exemplary embodiment of the present application;

FIG. 6 is a flowchart of a rear-end collision prevention control method, according to another exemplary embodiment of the present application;

FIG. 7 is a flowchart of a rear-end collision prevention control method, shown in another exemplary embodiment of the application;

FIG. 8 is a flowchart of a rear-end collision prevention control method, according to another exemplary embodiment of the present application;

FIG. 9 is a schematic flow chart of rear-end collision prevention control during vehicle travel in an exemplary application scenario;

fig. 10 is a block diagram of a rear-end collision prevention control apparatus shown in an exemplary embodiment of the present application;

Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.

In the present application, the term "plurality" means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

Firstly, it should be noted that in the evaluation of preventing rear-end collision, it is particularly important to accurately estimate the running risk of the vehicle, which is the key point of the real-time traffic safety information prompt system. In order to effectively prevent rear-end collisions, a number of factors need to be considered and corresponding measures taken.

First, the driver needs to pay attention to the safe distance around the vehicle at all times, particularly the distance from the following vehicle. By observing the rearview mirror, the distance and the running state of the rear vehicle can be judged. For example, when the hood of a rear vehicle is seen to be completely disappeared in the rear view mirror, it generally means that the front-rear vehicle distance is about 5 meters; while the front-rear vehicle distance is about 1 meter when the windshield of the rear vehicle can be seen. Such observation helps the driver to take evasive measures in time, such as slowing down, changing lanes or turning on brake lights, etc., to prevent rear-end collisions of the vehicle being driven.

Secondly, the safety performance of the vehicle is also a key for preventing rear-end collision of the rear vehicle. Modern automobiles are often equipped with a variety of active safety techniques, such as automatic braking systems, collision warning systems, lane keeping assist systems, and the like. These systems can monitor conditions around the vehicle in real time and alert the driver if necessary or automatically take emergency braking measures to avoid or reduce the occurrence of rear-end accidents.

In addition, the driving habit and skill of the driver are also important to preventing rear-end collision. The driver should follow the traffic rules, keep the safe distance with the preceding car, avoid actions such as sudden braking or suddenly changing the way to reduce the influence to the following car. Meanwhile, the driver should pay attention to the change of driving environment, such as weather, road conditions and the like, at any time so as to take countermeasures in time.

Fig. 1 is a schematic view showing an environment in which rear-end collision prevention control is performed during running of a vehicle according to an exemplary embodiment of the present application. As shown in fig. 1, during the running of the vehicle, the intelligent terminal 110 collects the rear vehicle image of the current vehicle according to the preset frequency, and then the collected rear vehicle image can be sent to the server 120, the server 120 determines the risk coefficient corresponding to the rear vehicle according to the received rear vehicle image, when the server 120 detects that the risk coefficient of the rear vehicle reaches above the preset risk coefficient threshold, the current risk level of the vehicle is determined based on the risk coefficient, the current running state of the vehicle is obtained, and then a corresponding avoidance strategy is generated based on the running state of the vehicle and the risk level of the vehicle, and then the vehicle is controlled to execute the avoidance strategy, so that the vehicle avoids the risk of being knocked into back.

The smart terminal 110 shown in fig. 1 may be any terminal device supporting installation of navigation map software, such as a smart phone, a vehicle-mounted computer, a tablet computer, a notebook computer, or a wearable device, but is not limited thereto. The server 120 shown in fig. 1 may be, for example, an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), and basic cloud computing services such as big data and artificial intelligence platforms, which are not limited herein. The intelligent terminal 110 may communicate with the server 120 through a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, which is not limited herein.

The existing rear-end collision prevention active safety control method mainly detects the distance and the relative speed between a rear vehicle and the vehicle by a radar or laser ranging mode, but the radar or laser ranging mode increases the production cost of the vehicle, and the matched radar is arranged at the front end of the vehicle, so that only the braking action of the front vehicle can be identified, the running state of the rear vehicle cannot be identified, and the early warning and avoiding effects of the vehicle are limited to a certain extent.

To solve these problems, embodiments of the present application propose a rear-end collision prevention control method, a rear-end collision prevention control apparatus, an electronic device, a computer-readable storage medium, and a computer program product, respectively, which will be described in detail below.

Referring to fig. 2, fig. 2 is a flowchart illustrating a rear-end collision prevention control method according to an exemplary embodiment of the present application. The method may be applied to the implementation environment shown in fig. 1 and executed specifically by the server 120 in the implementation environment. It should be understood that the method may be adapted to other exemplary implementation environments and be specifically executed by devices in other implementation environments, and the implementation environments to which the method is adapted are not limited by the present embodiment.

As shown in fig. 2, in an exemplary embodiment, the rear-end collision prevention control method at least includes steps S210 to S240, which are described in detail as follows:

in step S210, a rear vehicle image of the vehicle acquired at a preset frequency is acquired.

Specifically, the image of the rear vehicle may be acquired by an image acquisition device provided at the rear of the vehicle according to a preset frequency. For example, a high-definition camera is mounted on a vehicle to ensure that it can clearly capture images of the vehicle behind. At the same time, a timer or timer module is used to set a preset acquisition frequency, such as one acquisition per second or one acquisition per several minutes. Thus, each time the timer is triggered, the camera captures and saves a rear vehicle image. Or by advanced intelligent monitoring systems, which typically integrate cameras, sensors and data processing units. By programming the system can automatically capture images of the vehicle behind at a preset frequency and perform the necessary processing, such as image compression and storage.

Step S220, determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image.

Specifically, the number of vehicles existing behind the host vehicle, the type corresponding to the rear vehicle, the speed of the rear vehicle, the distance between the rear vehicle and the host vehicle, etc. may be determined according to the rear vehicle image, and then the risk coefficient caused by the host vehicle is determined according to the number of vehicles existing behind the host vehicle, the type corresponding to the rear vehicle, the speed of the rear vehicle, the distance between the rear vehicle and the host vehicle, etc.

Illustratively, first, the acquired rear vehicle image is preprocessed. This may include removing noise from the image, adjusting the size and brightness of the image, and possibly color correction to ensure accuracy of subsequent processing. The vehicle is detected from the preprocessed image using image processing techniques and machine learning algorithms. This typically involves edge detection, shape recognition, etc. steps to accurately locate and identify the vehicle in the image. The detected characteristics of the vehicle are extracted. These characteristics may include the size, position, speed, acceleration, etc. of the vehicle. These features will be used for subsequent risk factor evaluation. And according to the extracted vehicle characteristics, the safety risk coefficient is evaluated by combining the factors such as traffic rules, road conditions, weather conditions and the like. This may be accomplished by building a risk assessment model that may calculate a risk factor for each rear vehicle based on vehicle characteristics and other relevant factors.

And step S230, if the risk coefficient reaches the preset risk coefficient threshold or more, determining the risk level of the vehicle based on the risk coefficient.

Specifically, whether the corresponding risk coefficient of the rear vehicle reaches more than a preset risk coefficient threshold value or not can be detected, if the risk coefficient of the rear vehicle does not reach more than the preset risk coefficient threshold value, the rear vehicle is characterized as not constituting the rear-end collision risk to the own vehicle at present, if the risk coefficient of the rear vehicle reaches more than the preset risk coefficient threshold value, the rear-end collision risk to the own vehicle is characterized as being represented, the risk level of the rear-end collision risk to the own vehicle is determined according to the risk coefficient of the rear vehicle constituting the rear-end collision risk,

For example, first, a plurality of risk level thresholds may be set according to safety standards and actual requirements. These thresholds will be used to divide the risk factors into different risk levels. For example, three levels, low, medium, and high, may be set, each level corresponding to a range of risk factors. And comparing the calculated risk coefficient with a set risk level threshold. And determining the dangerous level of the vehicle according to the range of the dangerous coefficient. According to the comparison result, the risk level of the vehicle is determined as a level corresponding to the risk coefficient. For example, if the risk factor is within a threshold range of a medium level, the risk level of the vehicle is the medium level.

Step S240, acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the risk level of the vehicle, and controlling the vehicle to execute the avoidance strategy.

Specifically, a running state of the vehicle at the current moment may be obtained, where the running state of the vehicle includes a current vehicle speed of the vehicle, a vehicle acceleration, a steering wheel angle of the vehicle, a brake opening degree, and the like. And a corresponding avoidance strategy can be generated through the running state of the vehicle and the dangerous grade of the vehicle, and the vehicle is controlled to execute the avoidance strategy, so that the rear-end collision of the vehicle by the rear vehicle is avoided.

For example, a driving state of the vehicle at the current moment may be obtained, where the driving state includes various information, for example, a vehicle speed, positioning data of the vehicle, a direction of the vehicle, an acceleration of the vehicle, a steering angle of the vehicle, and obstacle information (for example, a front vehicle of the vehicle) around the vehicle, and then an avoidance strategy corresponding to the vehicle is generated according to the driving state information of the vehicle and a risk level generated by a rear vehicle, where the avoidance strategy may include: acceleration or deceleration, steering of the vehicle, lane changing of the vehicle, etc., and the selection and execution of these avoidance strategies should ensure as much as possible the safety of the vehicle and passengers while avoiding collisions with other road users. After the avoidance strategy is generated, the system may implement the strategy via a control system (e.g., brake system, steering system, powertrain system, etc.) of the vehicle. For example, if the strategy is deceleration, the system may be implemented by controlling the braking system of the vehicle. In the whole process, the system needs to continuously update the running state of the vehicle and adjust the avoidance strategy according to the new state so as to ensure that the vehicle can safely avoid dangers. Notably, such systems require extensive data training and optimization to ensure that the correct decisions can be made in all cases.

In this embodiment, the risk coefficient corresponding to the rear vehicle is determined according to the rear vehicle image acquired at the preset frequency, and when the risk coefficient reaches above the preset risk coefficient threshold, the rear vehicle information can be acquired through the simple image acquisition device, so that the cost for acquiring the rear vehicle information is saved, after that, the risk level of the vehicle can be determined according to the risk coefficient of the rear vehicle, and then the corresponding avoidance strategy is generated according to the risk level and the driving state of the current vehicle, so that the vehicle is controlled to execute the avoidance strategy, rear-end collision of the rear vehicle is prevented, and the driving safety is improved.

Further, based on the above embodiment, referring to fig. 3, in one exemplary embodiment of the present application, the specific implementation process of determining the risk coefficient corresponding to the rear vehicle based on the rear vehicle image may further include step S310 and step S320, which are described in detail below:

Step S310, determining the corresponding vehicle type and the driving state of the rear vehicle based on the rear vehicle image;

Step S320, determining a risk coefficient corresponding to the rear vehicle based on the vehicle type and the running state.

In particular, by analyzing images captured from cameras behind the vehicle, the type of vehicle behind may be identified, where the vehicle type may include a sedan, a van, a bus, a motorcycle, and the like. And may generally be implemented by image recognition techniques, such as deep learning algorithms, which are trained on a large amount of annotation data to identify different vehicle types in the image. In addition, by analyzing the image of the rear vehicle, the running state of the rear vehicle is identified, wherein the running state of the rear vehicle includes the speed of the rear vehicle, the distance from the own vehicle, the running direction, the acceleration, etc., and after determining the type and the running state of the rear vehicle, these factors can be further analyzed to evaluate the potential danger constituted by the rear vehicle to the own vehicle.

Furthermore, there are different potential hazards due to the different types of vehicles. For example, large trucks may have a higher risk factor due to their volume and mass, which may cause greater injury in the event of a collision. The speed, distance, acceleration and direction of travel of the rear vehicle all affect the risk factor. For example, if the rear vehicle is approaching the host vehicle quickly and is very close, the risk factor will be high. The calculation of the risk coefficient can be further adjusted by considering the current road condition, traffic rules (such as speed limit, overtaking prohibition, etc.), weather conditions, etc.

In this embodiment, the risk coefficient of the rear vehicle to the vehicle is determined according to the vehicle type and the driving state corresponding to the rear vehicle, so that the rear-end collision risk caused by the rear vehicle can be accurately determined, and the rear-end collision risk caused by the rear vehicle can be further avoided.

Further, based on the above embodiment, referring to fig. 4, in one exemplary embodiment of the present application, a specific implementation process of the rear-end collision prevention control method may further include step S410 and step S420, which are described in detail below:

step S410, acquiring a rear vehicle image set acquired in a preset period;

Step S420, calculates a running state corresponding to the rear vehicle based on the adjacent images in the rear vehicle image set.

In particular, the predetermined period may be a fixed length of time, which typically involves image processing and computer vision techniques such as feature extraction and tracking. Such as 1 second, 5 seconds, or 10 seconds, may also be a dynamic time period triggered based on a particular event. During this period, the camera behind the vehicle will continuously or intermittently capture images of the vehicle behind and save those images as a set of images. The image set contains visual information of the rear vehicle at different time points, and provides a data basis for subsequent analysis.

Further, by comparing the displacement of the rear vehicle in the adjacent images with the time interval between these images, the speed of the rear vehicle, the acceleration of the rear vehicle, the distance of the rear vehicle from the own vehicle, the steering angle of the rear vehicle, and the like can be estimated.

In this embodiment, the running state of the rear vehicle is calculated through the adjacent images in the rear vehicle image set acquired in the preset period, so that the running state of the rear vehicle can be accurately mastered, accurate data support is brought for further avoiding rear-end collision caused by the rear vehicle, and the driving safety of the user is improved.

Further, based on the above embodiment, referring to fig. 5, in one exemplary embodiment of the present application, the specific implementation process of calculating the driving state corresponding to the rear vehicle based on the adjacent images in the rear vehicle image set may further include step S510 and step S520, which are described in detail below:

Step S510, calculating the vehicle speed, the acceleration and the distance between the vehicle and the vehicle corresponding to the current moment of the rear vehicle based on the adjacent images;

in step S520, a running state corresponding to the rear vehicle is determined based on the vehicle speed, the acceleration, and the distance from the vehicle corresponding to the current time of the rear vehicle.

In particular, the speed of the rear vehicle may be estimated by comparing the displacement of the rear vehicle in the adjacent images with the time interval between the images. This typically involves image processing and computer vision techniques such as feature extraction and tracking; the acceleration of the rear vehicle can be further calculated by continuously observing the speed change of the rear vehicle in the plurality of pairs of adjacent images. This helps to understand the trend of dynamic changes in the rear vehicles; by analyzing the contour, the position, the possible turn signal states and other information of the rear vehicle in the adjacent images, whether the running direction of the rear vehicle is straight, steering or parking can be judged; the actual distance between the rear vehicle and the vehicle in the adjacent images can be estimated by using the camera calibration parameters and the image processing technology. Further, the running state of the rear vehicle can be determined based on the vehicle speed, acceleration, and distance from the vehicle.

It should be noted that this calculation method is affected by various factors, such as resolution of the camera, view angle, light condition, and algorithm accuracy of image processing. Therefore, in practical applications, it is necessary to continuously optimize the algorithm and calibrate the system parameters to improve the accuracy and reliability of the running state calculation. Meanwhile, how to deal with abnormal situations, such as the situation that a camera is blocked or the image quality is poor, needs to be considered, so as to ensure the robustness and the stability of the system.

In this embodiment, the vehicle speed, the acceleration and the distance between the vehicle and the vehicle corresponding to the current moment of the rear vehicle are calculated through the adjacent images, so that the running dynamic information of the rear vehicle can be rapidly and accurately determined, data support is provided for the vehicle to prevent rear-end collision and avoid danger, driving safety is further improved, and user experience is improved.

Further, based on the above embodiment, referring to fig. 6, in one exemplary embodiment of the present application, the driving status further includes road information, and the specific implementation process of the rear-end collision prevention control method may further include steps S610 to S630, which are described in detail below:

In step S610, the road type, the geometric feature of the road, the vehicle speed and acceleration corresponding to the current time of the rear vehicle, and the distance between the rear vehicle and the vehicle are weighted, so as to obtain a calculation result, and the dynamic risk factor of the rear vehicle is determined according to the calculation result.

In particular, the road information may include speed limit rules for the current road, which depend on the particular road and location where the vehicle is located. Different road types (e.g., expressways, urban streets, rural roads, etc.) and specific road segments (e.g., school areas, construction areas, etc.) may have different speed limit regulations. Therefore, in order to obtain the most accurate current speed limit information, the real-time road information should be obtained with reference to local traffic signs and regulations or using a navigation system or the like. In addition, the current weather condition, the front vehicle condition and the like and the corresponding vehicle speed, acceleration and the distance between the acceleration and the vehicle can be considered, and the danger degree brought by different factors to the vehicle is different, so that weighting calculation can be carried out according to different weighting coefficients, and further the dynamic danger factor of the rear vehicle can be determined according to the weighting calculation result.

By way of example, the speed limit rule of the current road, the geometric feature of the current road, the current road surface condition and the like can be obtained, wherein the geometric feature of the road comprises: curvature, width, gradient, etc. of the current road, which affect the running stability and safety of the vehicle; road conditions include, for example, wet skid, potholes, snow and ice, etc., which affect the braking and handling performance of the vehicle.

Then, the speed difference between the host vehicle and the rear vehicle is calculated, wherein the larger the speed difference is, the closer the rear vehicle is to the host vehicle, the higher the potential risk is, and the acceleration of the rear vehicle is taken into consideration. If the rear vehicle is accelerating and the acceleration is large, it may approach the host vehicle faster, increasing the risk of collision. Conversely, if the rear vehicle is decelerating, the risk thereof may be reduced. The distance between the rear vehicle and the vehicle is a key index for evaluating the risk. The closer the distance, the greater the potential for collisions, and thus the risk factor should be increased accordingly. The road information is taken into account. For example, if the current road speed limit is low and the speed of the rear vehicle is far above the speed limit, then its risk factor should be increased accordingly. Also, if road conditions are bad (e.g., wet road), the braking distance of the vehicle increases, and thus the assessment of the risk of the rear vehicle should be increased. Thus, the dynamic risk factors of the rear vehicles are comprehensively calculated.

Step S620, determining a static risk factor of the rear vehicle based on the vehicle type corresponding to the rear vehicle.

Specifically, a static risk factor brought by the rear vehicle to the vehicle may be determined according to a vehicle type corresponding to the rear vehicle, where the vehicle type may include: bicycles, motorcycles, tricycles, automobiles, trucks, trailers, and the like, as shown in table 1 below, the static risk factors for different vehicle types are different.

TABLE 1

In step S630, a risk factor of the vehicle is determined based on the static risk factor and the dynamic risk factor.

In particular, the static risk factor and the dynamic risk factor may be combined to determine the overall risk factor of the vehicle. For example, this may be achieved by simple weighted summation, product or other more complex mathematical operations. The specific calculation method can be customized according to the actual requirements and the security policy. For example, the static risk factor and the dynamic risk factor may be multiplied by the respective weights, and then the results may be added to obtain the risk coefficient.

In this embodiment, a dynamic risk factor generated by the rear vehicle for the vehicle is determined according to the running state of the rear vehicle, and a static risk factor generated by the rear vehicle for the vehicle is determined according to the vehicle type corresponding to the rear vehicle, so that a risk coefficient of the vehicle can be determined according to the dynamic risk factor and the static risk factor of the vehicle, and further corresponding avoidance measures are executed to avoid rear-end collision by the rear vehicle, thereby improving driving safety.

Further, based on the above embodiment, referring to fig. 7, in one exemplary embodiment of the present application, the specific implementation process of controlling the vehicle to execute the avoidance strategy based on the risk level of the vehicle and the running state of the vehicle may further include step S710 and step S720, which are described in detail below:

in step S710, if the risk level does not reach the preset risk level or higher, a presentation message is generated based on the running state of the vehicle.

Specifically, if the risk level does not reach above the preset risk level, this means that the current rear vehicle has a certain potential threat, but the risk level is not enough to trigger an emergency risk avoidance measure or alarm. In this case, in order to alert the driver to pay attention and take corresponding precautions, a prompt message may be generated based on the running state of the vehicle. The vehicle can be noticed by a simple prompt, such as "please pay attention to the rear vehicle, keep a safe distance", or a detailed prompt, such as "a large truck is approaching quickly behind, please keep vigilance, slow down or change lanes in time". "; or according to the driving habit of the driver, personalized prompts are provided, such as 'according to the usual driving style, the prompt of early deceleration and the safe overtaking of the rear vehicle', and the prompt information can be transmitted to the driver through the display screen, the sound prompt or the vibration feedback of the vehicle. In order to ensure that the driver can timely notice the prompt information, the frequency and the intensity of the prompt can be adjusted according to the driving state and the road environment. In generating the prompt information, care should also be taken to avoid overload of the information or too frequent prompts so as not to interfere with the normal driving of the driver. Therefore, the triggering condition of the prompt and the presentation mode of the prompt information need to be set reasonably.

Step S720, if the dangerous level reaches more than the preset dangerous level, generating an avoidance strategy based on the running state of the vehicle, and controlling the vehicle to execute the avoidance strategy.

Specifically, when the dangerous level reaches above the preset dangerous level, it means that the threat of the rear vehicle to the vehicle is serious enough, and an avoidance measure needs to be immediately taken to ensure driving safety. In this case, the system will generate avoidance strategies based on the driving conditions of the vehicle and control the vehicle to execute these strategies.

For example, in generating the avoidance strategy, the current vehicle driving conditions must be considered: including vehicle speed, acceleration, direction of travel, lane position, etc.; dynamic characteristics of the rear vehicle: dynamic information such as vehicle speed, acceleration, distance and the like directly influences the selection of the avoidance strategy. For example, if the rear vehicle is very fast and very close in distance, more urgent avoidance measures may be required; road environment: geometric features of the road (e.g., curvature, width), traffic flow, traffic signs and signals, weather conditions, etc., all affect the effectiveness of the avoidance strategy. For example, emergency braking on wet roads may lead to uncontrolled vehicle movement, and thus a safer avoidance mode needs to be selected, and the handling performance of the vehicle is also: different types and configurations of vehicles have different handling characteristics including braking distance, acceleration characteristics, steering stability, etc. The system needs to generate a proper avoidance strategy according to the actual situation of the vehicle. To generate the following avoidance strategies: deceleration avoidance: the collision risk is reduced by increasing the distance to the vehicle behind by reducing the vehicle speed. This is generally applicable where the rear vehicle speed is not fast or road conditions do not allow for an abrupt change of track; lane change avoidance: if conditions allow (e.g., no vehicles or obstacles in adjacent lanes), the system may control the vehicle to lane-change to avoid the rear dangerous vehicle. The system is required to rapidly and accurately judge the feasibility and the safety of the lane change; emergency braking: in extreme cases, if other avoidance measures are not available or sufficient to avoid a collision, the system may take emergency braking to minimize the impact force of the collision.

In this embodiment, different avoidance strategies are generated through different hazard levels caused by the rear vehicles, so that the rear-end collision avoidance mode is more diversified and personalized, and the use experience of the user is improved.

Further, based on the above embodiment, referring to fig. 8, in one exemplary embodiment provided by the present application, the specific implementation process of generating the avoidance strategy based on the driving status of the vehicle may further include step S810 and step S820, which are described in detail below:

step S810, determining avoidance parameters of the vehicle based on the running state of the vehicle and the danger coefficient, wherein the avoidance parameters comprise steering wheel rotation angle and acceleration;

step S820, generating an avoidance strategy based on steering wheel angle and acceleration.

Specifically, the avoidance parameters of the vehicle can be determined according to the running state of the vehicle and the corresponding danger coefficient of the rear vehicle, wherein the avoidance parameters include steering wheel rotation angle and acceleration of the vehicle. Among other factors affecting the steering wheel angle of the vehicle may include: current vehicle speed of own vehicle: the faster the vehicle speed, the greater the steering angle may be required to change the direction of travel in a shorter time; road width and curvature: narrower roads or greater road curvatures may require smaller steering angles to avoid vehicles driving off the road or rollover; risk coefficient: the higher the risk factor, the more urgent steering angle may be selected to avoid the rear vehicle more quickly.

Furthermore, acceleration determines the degree of acceleration or deceleration of the vehicle, which is also important for the avoidance strategy. In determining acceleration, consideration is needed to: current vehicle speed and desired avoidance distance: the higher the vehicle speed and the shorter the required avoiding distance, the greater the acceleration may be required to quickly adjust the vehicle speed; road conditions and vehicle performance: a wet road or a vehicle with poor braking performance may require less acceleration to avoid tire slip or runaway; driver intention: if the system is able to recognize the driving intention of the driver, the magnitude of the acceleration may be adjusted in conjunction with the driver's preference.

Then, according to the steering wheel angle and the current position of the vehicle, the system can calculate an avoidance path to ensure that the vehicle can safely avoid the rear vehicle, and can control the vehicle to run along the avoidance path according to the planned speed by adjusting the acceleration. This may involve accelerating, decelerating or maintaining the current speed. In addition, in the avoidance process, the system should continuously monitor the road and vehicle states, and adjust steering wheel rotation angle and acceleration in real time according to actual conditions so as to ensure the effectiveness of the avoidance strategy.

In the embodiment, the avoidance of the rear-end collision risk of the rear vehicle is realized by changing the steering wheel angle and the acceleration of the vehicle, and the steering wheel angle and the acceleration of the vehicle are adjusted in real time by detecting the states of the road and the vehicle, so that the effectiveness of the avoidance strategy is improved, and the driving safety of the vehicle is improved.

Fig. 9 is a schematic flow chart of rear-end collision prevention control in an exemplary application scenario. In the application scene shown in 9, acquiring rear vehicle images of the vehicle according to a preset frequency, and acquiring a rear vehicle image set acquired in a preset period; the vehicle type corresponding to the rear vehicle can be determined according to the rear vehicle image set, and then the vehicle speed, the acceleration and the distance from the vehicle corresponding to the current moment of the rear vehicle are calculated based on the adjacent images in the rear vehicle image set; determining a dynamic risk factor of the rear vehicle based on the road information in the current running state, the vehicle speed, the acceleration and the distance from the vehicle, which correspond to the current moment of the rear vehicle; determining a static risk factor of the rear vehicle based on the vehicle type corresponding to the rear vehicle; the risk factor of the vehicle is determined based on the static risk factor and the dynamic risk factor. If the risk coefficient reaches more than a preset risk coefficient threshold value, determining the risk level of the vehicle based on the risk coefficient; and acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy. Please refer to the descriptions in the foregoing embodiments for detailed implementation, and a detailed description thereof is omitted herein.

Fig. 10 is a block diagram of a rear-end collision prevention control apparatus shown in an exemplary embodiment of the present application. The apparatus may be applied to the implementation environment shown in fig. 1 and is specifically configured in the server 120. The apparatus may also be adapted to other exemplary implementation environments and may be specifically configured in other devices, and the present embodiment is not limited to the implementation environments to which the apparatus is adapted.

As shown in fig. 10, the exemplary rear-end collision prevention control apparatus 1000 includes: an acquisition module 1010, configured to acquire a rear vehicle image acquired at a preset frequency; a first determining module 1020 configured to determine a risk coefficient corresponding to the rear vehicle based on the rear vehicle image; a second determining module 1030, configured to determine a risk level of the vehicle based on the risk coefficient if the risk coefficient reaches above a preset risk coefficient threshold; the control module 1040 is configured to obtain a driving state of the vehicle, generate an avoidance policy based on the driving state of the vehicle and a risk level of the vehicle, and control the vehicle to execute the avoidance policy.

According to an aspect of the embodiment of the present application, the first determining module 1020 is further configured to determine a vehicle type and a driving state corresponding to the rear vehicle based on the rear vehicle image; and determining a corresponding risk coefficient of the rear vehicle based on the vehicle type and the driving state.

According to an aspect of the application embodiment, the rear-end collision prevention control device further includes: the image set acquisition module is used for acquiring a rear vehicle image set acquired in a preset period; and the calculation module is used for calculating the corresponding running state of the rear vehicle based on the adjacent images in the rear vehicle image set.

According to an aspect of the embodiment of the present application, the calculation module is further configured to calculate a vehicle speed, an acceleration, and a distance from the vehicle corresponding to a current time of the rear vehicle based on the adjacent images; the running state corresponding to the rear vehicle is determined based on the vehicle speed, the acceleration, and the distance from the vehicle corresponding to the current time of the rear vehicle.

According to an aspect of the embodiment of the present application, the first calculation module 1020 is further configured to perform weighted calculation on a road type, a geometric feature of the road, a vehicle speed and an acceleration corresponding to a current moment of a rear vehicle, and a distance between the rear vehicle and the vehicle, obtain a calculation result, and determine a dynamic risk factor of the rear vehicle according to the calculation result; determining a static risk factor of the rear vehicle based on the vehicle type corresponding to the rear vehicle; the risk factor of the vehicle is determined based on the static risk factor and the dynamic risk factor.

According to an aspect of the embodiment of the present application, the control module 1040 is further configured to generate a prompt message based on a driving state of the vehicle if the risk level does not reach above a preset risk level; and if the dangerous level reaches above the preset dangerous level, generating an avoidance strategy based on the running state of the vehicle, and controlling the vehicle to execute the avoidance strategy.

According to an aspect of the embodiment of the present application, the control module 1040 is further configured to determine, based on a driving state of the vehicle and a risk coefficient, an avoidance parameter of the vehicle, where the avoidance parameter includes a steering angle and an acceleration; and generating an avoidance strategy based on the steering wheel angle and the acceleration.

It should be noted that, the rear-end collision prevention control device provided by the foregoing embodiment and the rear-end collision prevention control method provided by the foregoing embodiment belong to the same concept, and specific manners in which the respective modules and units perform operations have been described in detail in the method embodiment, which is not repeated herein. In practical application, the rear-end collision prevention control device provided in the above embodiment may distribute the functions to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.

The embodiment of the application also provides electronic equipment, which comprises: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic apparatus to implement the rear-end collision prevention control method provided in each of the above embodiments.

Fig. 11 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application. It should be noted that, the computer system 1100 of the electronic device shown in fig. 11 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.

As shown in fig. 11, the computer system 1100 includes a central processing unit (Central Processing Unit, CPU) 1101 that can perform various appropriate actions and processes, such as performing the methods in the above-described embodiments, according to a program stored in a Read-Only Memory (ROM) 1102 or a program loaded from a storage portion 1108 into a random access Memory (Random Access Memory, RAM) 1103. In the RAM 1103, various programs and data required for system operation are also stored. The CPU 1101, ROM 1102, and RAM 1103 are connected to each other by a bus 1104. An Input/Output (I/O) interface 1105 is also connected to bus 1104.

The following components are connected to the I/O interface 1105: an input section 1106 including a keyboard, a mouse, and the like; an output portion 1107 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 1108 including a hard disk or the like; and a communication section 1109 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1109 performs communication processing via a network such as the internet. The drive 1110 is also connected to the I/O interface 1105 as needed. Removable media 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed on drive 1110, so that a computer program read therefrom is installed as needed into storage section 1108.

In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1109, and/or installed from the removable media 1111. When executed by a Central Processing Unit (CPU) 1101, performs the various functions defined in the system of the present application.

It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

Another aspect of the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the rear-end collision prevention control method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.

Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device executes the rear-end collision prevention control method provided in the above-described respective embodiments.

The foregoing is merely illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art can easily make corresponding variations or modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be defined by the claims.

Claims (10)

1. The rear-end collision prevention control method is characterized by comprising the following steps of:

acquiring a rear vehicle image of a vehicle acquired according to a preset frequency;

determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image;

If the risk coefficient reaches more than a preset risk coefficient threshold value, determining the risk level of the vehicle based on the risk coefficient;

And acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy.

2. The method of claim 1, wherein the determining the risk coefficient corresponding to the rear vehicle based on the rear vehicle image comprises:

determining a vehicle type and a driving state corresponding to the rear vehicle based on the rear vehicle image;

and determining a corresponding risk coefficient of the rear vehicle based on the vehicle type and the driving state.

3. The method of claim 2, wherein the method further comprises:

acquiring a rear vehicle image set acquired in a preset period;

and calculating the corresponding running state of the rear vehicle based on the adjacent images in the rear vehicle image set.

4. The method of claim 3, wherein the calculating the corresponding travel state of the rear vehicle based on adjacent images in the set of rear vehicle images comprises:

Calculating the vehicle speed, the acceleration and the distance between the vehicle and the vehicle corresponding to the current moment of the rear vehicle based on the adjacent images;

And determining a running state corresponding to the rear vehicle based on the vehicle speed, the acceleration and the distance between the rear vehicle and the vehicle.

5. The method of claim 2, wherein the driving status further comprises road information including a road type and a geometric feature of a road; the method further comprises the steps of:

Weighting calculation is carried out on the road type, the geometric feature of the road, the vehicle speed and acceleration corresponding to the current moment of the rear vehicle and the distance between the rear vehicle and the vehicle to obtain a calculation result, and a dynamic risk factor of the rear vehicle is determined according to the calculation result;

Determining a static risk factor of the rear vehicle based on the vehicle type corresponding to the rear vehicle;

a risk factor for the vehicle is determined based on the static risk factor and the dynamic risk factor.

6. The method of claim 1, wherein the controlling the vehicle to perform an avoidance strategy based on the hazard level of the vehicle and the driving status of the vehicle comprises:

if the dangerous level does not reach the preset dangerous level, generating prompt information based on the running state of the vehicle;

And if the dangerous level reaches above a preset dangerous level, generating an avoidance strategy based on the running state of the vehicle, and controlling the vehicle to execute the avoidance strategy.

7. The method of claim 1, wherein the generating an avoidance strategy based on the driving conditions of the vehicle comprises:

Determining avoidance parameters of the vehicle based on the running state of the vehicle and the risk coefficient, wherein the avoidance parameters comprise steering wheel rotation angle and acceleration;

and generating the avoidance strategy based on the steering wheel angle and the acceleration.

8. A rear-end collision prevention control device, characterized in that the device comprises:

the acquisition module is used for acquiring rear vehicle images acquired according to a preset frequency;

The first determining module is used for determining a risk coefficient corresponding to the rear vehicle based on the rear vehicle image;

The second determining module is used for determining the risk level of the vehicle based on the risk coefficient if the risk coefficient reaches above a preset risk coefficient threshold value;

the control module is used for acquiring the running state of the vehicle, generating an avoidance strategy based on the running state of the vehicle and the dangerous grade of the vehicle, and controlling the vehicle to execute the avoidance strategy.

9. An electronic device, comprising:

One or more processors;

Storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the rear-end collision prevention control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the rear-end collision prevention control method according to any one of claims 1 to 7.