Vehicle control method, device, system and equipment

Abstract

The invention relates to a vehicle control method, device, system and equipment, and relates to the technical field of automatic driving. The method comprises the following steps: when a vehicle is in an automatic driving mode and a lane line on a current driving road of the vehicle cannot be recognized, acquiring a driving track of a candidate vehicle driving in the same direction in front of the vehicle; determining a target vehicle in the candidate vehicles based on the driving tracks of the candidate vehicles; the target vehicle is the vehicle with the highest parallelism in the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelism of the target vehicle and the trajectory parallelism of other vehicles in the candidate vehicles; the trajectory parallelism is used for representing the parallelism degree between the driving trajectories of the two vehicles; and controlling the vehicle to run according to the running track of the target vehicle. Therefore, the problem of interruption or instability of the automatic driving mode caused by disappearance of the lane line can be avoided, and the adaptability and robustness of the intelligent driving system in various complex road environments are improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, in particular to the field of following vehicles, and specifically to a vehicle control method, device, system and equipment. Background Art

[0002] Driving assistance technology has gradually become standard for today's mid-to-high-end models. For example, the currently popular level 2 (L2) functional lanes are widely used. However, in actual applications, complex scenarios such as intersections, lane line wear, road maintenance, and backlight often make it difficult for on-board cameras to accurately identify lane lines. Especially when the autonomous driving vehicle is not equipped with a high-precision map, or the relevant lane lines disappear and are not covered by the high-precision map, most autonomous driving systems will prompt the driver to take over driving, thereby interrupting the autonomous driving function. This problem not only destroys the continuity of intelligent driving, but also greatly reduces the user's driving experience.

[0003] The related technology proposes a method and system for an unmanned vehicle to complete the correct passage of the vehicle at each specific intersection under different circumstances in the absence of accurate maps, large navigation errors and a large number of intersections. However, the related technology requires the pre-collection of the driving path data of the intersection. If an intersection without data is encountered, the vehicle will not be able to pass.

[0004] Related technologies have proposed to deal with the scenario where lane lines disappear based on high-precision maps and high-precision positioning. However, in the event of areas not covered by high-precision maps, or high-precision map errors and positioning deviations, these may cause functional failures and result in low robustness. Summary of the invention

[0005] The present application provides a vehicle control method, device, system and equipment to at least solve the technical problem of interruption or instability of the automatic driving mode caused by the disappearance of lane lines in the related art. The technical solution of the present application is as follows:

[0006] According to the first aspect provided by the present application, a vehicle control method is provided, including: when the vehicle is in an automatic driving mode and the lane line on the road on which the vehicle is currently traveling cannot be identified, obtaining a driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle; based on the driving trajectory of the candidate vehicles, determining a target vehicle among the candidate vehicles; the target vehicle is the vehicle with the highest parallelism among the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelism of the target vehicle and other vehicles among the candidate vehicles; the trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of two vehicles; and controlling the vehicle to travel according to the driving trajectory of the target vehicle.

[0007] According to the above-mentioned technical means, when the lane lines cannot be identified, the present application can maintain a relatively stable driving state by following the candidate vehicle with the highest degree of parallelism, avoiding the risk of deviating from the road or colliding with other vehicles due to unclear lane lines, and avoiding the interruption or instability of the automatic driving mode due to the disappearance of lane lines, thereby improving the adaptability and robustness of the intelligent driving system in various complex road environments.

[0008] In addition, traditional autonomous driving systems often rely on high-precision maps for accurate positioning and navigation. However, high-precision maps are expensive to obtain and update, and may not be covered in some areas. By following the vehicle in front, the reliance on high-precision maps can be reduced to a certain extent, improving the popularity and practicality of autonomous driving systems.

[0009] In one possible approach, the candidate vehicle satisfies one or more of the following: the distance between the candidate vehicle and the vehicle is less than a first distance threshold; the orientation angle difference between the candidate vehicle and the vehicle is less than a first angle threshold; the speed is greater than a speed threshold; and the lateral deviation between the candidate vehicle and the estimated trajectory is less than a deviation threshold.

[0010] Based on the above technical means, the present application can ensure that the distance between the candidate vehicle and the own vehicle is within a safe range, reduce the risk of collision accidents such as rear-end collisions, and by limiting lateral deviations, etc., ensure that the candidate vehicle will not deviate too far from the estimated trajectory, thereby reducing traffic accidents caused by deviation from the road.

[0011] In one possible manner, controlling the vehicle to travel along the driving trajectory of the target vehicle includes: controlling the vehicle to move from a current position to a starting point of the driving trajectory of the target vehicle; after controlling the vehicle to move to the starting point, controlling the vehicle to travel along the driving trajectory of the target vehicle.

[0012] According to the above-mentioned technical means, the present application can ensure that the vehicle transitions to the target trajectory in a smoother manner by moving to the starting point of the trajectory first, reduce abrupt acceleration changes, improve the riding experience, and avoid when the vehicle starts to move from the current position, directly jumping to the driving trajectory of the target vehicle may produce large acceleration or deceleration changes, which may affect the comfort of the passengers.

[0013] In one possible method, controlling a vehicle to move from a current position to a starting point of a driving trajectory of a target vehicle includes: obtaining the current position, heading angle, and steering wheel angle of the vehicle; determining a predicted driving trajectory from the current position to the starting point based on the current position, heading angle, steering wheel angle, and starting point; and controlling the vehicle to travel from the current position to the starting point according to the predicted driving trajectory.

[0014] According to the above technical means, the present application can accurately predict the driving trajectory and flexibly plan the path, so that the vehicle can travel to the starting point of the target trajectory in a smoother manner, reducing bumps and acceleration changes during driving.

[0015] In one possible method, based on the current position, heading angle, steering wheel angle and starting point, a predicted driving trajectory from the current position to the starting point is determined, including: based on the current position, heading angle, steering wheel angle, determining multiple trajectory points from the current position to the starting point; fitting multiple trajectory points to obtain a predicted driving trajectory.

[0016] According to the above technical means, the present application can obtain a smooth and continuous predicted driving trajectory by fitting multiple trajectory points, which helps the autonomous driving system to more accurately understand and predict the vehicle's motion state.

[0017] In one possible approach, when a vehicle is in an automatic driving mode, lane lines on the road on which the vehicle is currently traveling cannot be identified, and no candidate vehicle traveling in the same direction in front of the vehicle is identified, current trajectory information of the vehicle is obtained; trajectory calculation is performed based on the current trajectory information to obtain an estimated trajectory; and the vehicle is controlled to travel according to the estimated trajectory.

[0018] According to the above-mentioned technical means, in the case where the lane line cannot be identified or the vehicle ahead is not identified, the autonomous driving system can continue to make calculations based on the vehicle's current trajectory information, thereby maintaining the continuity of driving and preventing the vehicle from suddenly stopping or making other dangerous actions due to the inability to obtain environmental information.

[0019] In one possible approach, when the vehicle is in automatic driving and lane lines on the road on which the vehicle is traveling can be identified, an initial trajectory is determined based on the lane lines; an obstacle avoidance trajectory is determined based on the initial trajectory and obstacles detected by the vehicle; the obstacle avoidance trajectory is used to avoid obstacles; and when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than a second distance threshold, the vehicle is controlled to travel according to the obstacle avoidance trajectory.

[0020] According to the above technical means, the present application can timely plan an obstacle avoidance trajectory when an obstacle is detected, so that the vehicle can safely avoid the obstacle and avoid collision. By limiting the maximum deviation between the initial trajectory and the obstacle avoidance trajectory, it can ensure that the vehicle will not deviate too far from the lane line during the obstacle avoidance process, thereby maintaining driving stability.

[0021] In one possible approach, when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is greater than or equal to a second distance threshold, the vehicle is switched from the automatic driving mode to the manual driving mode.

[0022] Based on the above technical means, the present application can switch to manual driving mode in a timely manner to allow the driver to take over vehicle control when the autonomous driving system is unable to complete obstacle avoidance operations while ensuring safety, thereby avoiding potential safety risks.

[0023] According to a second aspect provided by the present application, a vehicle control device is provided, including: an acquisition unit, a determination unit and a control unit; the acquisition unit is used to acquire the driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle when the vehicle is in an automatic driving mode and the lane line on the road on which the vehicle is currently traveling cannot be identified; the determination unit is used to determine a target vehicle among the candidate vehicles based on the driving trajectory of the candidate vehicles; the target vehicle is the vehicle with the highest parallelism among the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelism of the target vehicle and other vehicles among the candidate vehicles; the trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of two vehicles; the control unit is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0024] In one possible manner, the control unit is specifically used to: control the vehicle to move from a current position to a starting point of a driving trajectory of a target vehicle; after controlling the vehicle to move to the starting point, control the vehicle to travel according to the driving trajectory of the target vehicle.

[0025] In one possible manner, the control unit is specifically used to: obtain the current position, heading angle and steering wheel angle of the vehicle; determine the predicted driving trajectory from the current position to the starting point based on the current position, heading angle, steering wheel angle and starting point; control the vehicle to travel from the current position to the starting point according to the predicted driving trajectory.

[0026] In one possible manner, the control unit is specifically used to: determine multiple trajectory points from the current position to the starting point based on the current position, heading angle, and steering wheel angle; and fit the multiple trajectory points to obtain a predicted driving trajectory.

[0027] In one possible manner, the acquisition unit is further used to obtain the vehicle's current trajectory information when the vehicle is in automatic driving mode, the lane lines on the road the vehicle is currently traveling on cannot be identified, and no candidate vehicle traveling in the same direction in front of the vehicle is identified; the determination unit is further used to perform trajectory calculation based on the current trajectory information to obtain an estimated trajectory; and the control unit is further used to control the vehicle to travel according to the calculated trajectory.

[0028] In one possible manner, the determination unit is further used to determine an initial trajectory based on lane lines when the vehicle is in automatic driving and lane lines on the road on which the vehicle is traveling can be identified; the determination unit is further used to determine an obstacle avoidance trajectory based on the initial trajectory and obstacles detected by the vehicle; the obstacle avoidance trajectory is used to avoid obstacles; the control unit is further used to control the vehicle to travel according to the obstacle avoidance trajectory when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than a second distance threshold.

[0029] In one possible embodiment, the control unit is further configured to switch the vehicle from the automatic driving mode to the manual driving mode when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is greater than or equal to a second distance threshold.

[0030] According to a third aspect provided by the present application, a vehicle control system is provided, including: a data acquisition device and a vehicle control device; the data acquisition device is used to collect the driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle when the vehicle is in an automatic driving mode and the lane line on the road on which the vehicle is currently traveling cannot be identified; the vehicle control device is used to determine a target vehicle among the candidate vehicles based on the driving trajectory of the candidate vehicles collected by the data acquisition device; the target vehicle is the vehicle with the highest parallelism among the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelism of the target vehicle and other vehicles among the candidate vehicles; the trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of two vehicles; the vehicle control device is also used for a control unit, which is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0031] According to the fourth aspect provided by the present application, an electronic device is provided, comprising: a processor; a memory for storing processor executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0032] According to the fifth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0033] According to the sixth aspect provided by the present application, a computer program product is provided, the computer program product comprising computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation manner thereof.

[0034] Therefore, the above technical features of the present application have the following beneficial effects:

[0035] (1) When lane lines cannot be identified, the vehicle can maintain a relatively stable driving state by following the candidate vehicle with the highest degree of parallelism, avoiding the risk of deviating from the road or colliding with other vehicles due to unclear lane lines, and avoiding the interruption or instability of the autonomous driving mode due to the disappearance of lane lines, thereby improving the adaptability and robustness of the intelligent driving system in various complex road environments.

[0036] In addition, traditional autonomous driving systems often rely on high-precision maps for accurate positioning and navigation. However, high-precision maps are expensive to obtain and update, and may not be covered in some areas. By following the vehicle in front, the reliance on high-precision maps can be reduced to a certain extent, improving the popularity and practicality of autonomous driving systems.

[0037] (2) It can ensure that the distance between the candidate vehicle and the vehicle is within a safe range, reducing the risk of rear-end collisions and other collisions. By limiting lateral deviation, it can ensure that the candidate vehicle does not deviate too far from the estimated trajectory, thereby reducing traffic accidents caused by deviation from the road.

[0038] (3) By moving to the starting point of the trajectory first, the vehicle can ensure that it transitions to the target trajectory in a smoother manner, reduce abrupt acceleration changes, improve the riding experience, and avoid the situation where the vehicle jumps directly to the target vehicle's driving trajectory when starting to move from the current position, which may cause large acceleration or deceleration changes and affect the comfort of the passengers.

[0039] (4) It can accurately predict the driving trajectory and flexibly plan the path, so that the vehicle can travel to the starting point of the target trajectory in a smoother manner, reducing bumps and acceleration changes during driving.

[0040] (5) By fitting multiple trajectory points, a smooth and continuous predicted driving trajectory can be obtained, which helps the autonomous driving system to understand and predict the vehicle's motion state more accurately.

[0041] (6) When the lane line cannot be identified or the vehicle ahead is not identified, the autonomous driving system can continue to make calculations based on the vehicle's current trajectory information, thereby maintaining driving continuity and preventing the vehicle from suddenly stopping or making other dangerous actions due to the inability to obtain environmental information.

[0042] (7) When an obstacle is detected, an obstacle avoidance trajectory can be planned in a timely manner, allowing the vehicle to safely avoid the obstacle and avoid collision. By limiting the maximum deviation between the initial trajectory and the obstacle avoidance trajectory, it can be ensured that the vehicle will not deviate too far from the lane line during the obstacle avoidance process, thereby maintaining driving stability.

[0043] (8) When the autonomous driving system is unable to complete obstacle avoidance operations while ensuring safety, the system can be switched to manual driving mode in a timely manner to allow the driver to take over vehicle control, thereby avoiding potential safety risks.

[0044] It should be noted that the technical effects brought about by any implementation method in the second to sixth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0047] Figure 1 is a schematic structural diagram of a vehicle according to an exemplary embodiment;

[0048] Figure 2 is a flow chart of a vehicle control method according to an exemplary embodiment;

[0049] Figure 3 is a schematic diagram showing a vehicle following process according to an exemplary embodiment;

[0050] Figure 4 is a schematic diagram showing an automatic driving application scenario when lane lines cannot be identified according to an exemplary embodiment;

[0051] Figure 5 is a schematic diagram of an automatic driving application scenario when lane lines can be identified according to an exemplary embodiment;

[0052] Figure 6 is a block diagram of a vehicle control device according to an exemplary embodiment;

[0053] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0054] In order to enable ordinary persons in the art to better understand the technical solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0056] In the embodiments of the present application, words such as "exemplary", "such as", or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary", "such as", or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "such as", or "for example" is intended to present related concepts in a specific way.

[0057] First, the relevant technologies involved in this application are explained to facilitate understanding by those skilled in the art.

[0058] Driving assistance technology has become an indispensable standard configuration for today's mid-to-high-end models. Among them, L2-level autonomous driving functions, such as adaptive cruise control and lane keeping assist systems, have been widely popularized and applied due to their significant advantages in improving driving safety and convenience. These technologies integrate advanced sensors, cameras and complex algorithms to enable vehicles to autonomously control direction, speed and even braking to a certain extent, greatly reducing the driving burden on drivers.

[0059] However, in the actual road driving environment, driving assistance technology faces the test of many complex scenarios. Especially at urban intersections, when lane lines are worn and blurred due to long-term use, when roads are undergoing maintenance and renovation, or when encountering strong backlight conditions, the on-board camera, as the "eyes" of the autonomous driving system, often has difficulty accurately and stably identifying lane lines. This decline in recognition ability directly affects the decision-making accuracy of the autonomous driving system, making it difficult to continue to maintain the original autonomous driving state.

[0060] In addition, when the autonomous driving vehicle is not equipped with a high-precision map, or the lane line disappears due to construction or other reasons, and the high-precision map fails to be updated in time to cover these changes, most autonomous driving systems will actively prompt the driver to take over the driving rights for safety reasons, thus having to interrupt the autonomous driving function. This sudden switch not only destroys the continuity and smoothness of intelligent driving, but also greatly reduces the user's driving experience, and may even cause drivers to question the reliability of autonomous driving technology.

[0061] Therefore, how to solve the problem of lane line recognition in complex scenarios and improve the environmental adaptability and robustness of the autonomous driving system has become an important direction for the current research and development of autonomous driving technology. This not only requires further optimization of the performance of on-board cameras and improvement of the accuracy and speed of image recognition, but also requires the exploration of comprehensive perception solutions that integrate multiple sensor data (such as radar, lidar, etc.), as well as the use of advanced technologies such as deep learning to continuously improve the intelligent decision-making capabilities of the autonomous driving system. At the same time, strengthening the real-time update and maintenance of high-precision maps is also the key to ensuring that the autonomous driving system can operate stably under various road conditions.

[0062] The related technology proposes a method and system for an unmanned vehicle to complete the correct passage of the vehicle at each specific intersection under different circumstances in the absence of accurate maps, large navigation errors and a large number of intersections. However, the related technology requires the pre-collection of the driving path data of the intersection. If an intersection without data is encountered, the vehicle will not be able to pass.

[0063] Related technologies have proposed to deal with the scenario where lane lines disappear based on high-precision maps and high-precision positioning. However, in the event of areas not covered by high-precision maps, or high-precision map errors and positioning deviations, these may cause functional failures and result in low robustness.

[0064] Based on this, in order to solve the problem of poor continuity and smoothness of intelligent driving caused by the disappearance of lane lines, the present application provides a vehicle control method, which can obtain the driving trajectory of the candidate vehicle traveling in the same direction in front of the vehicle when the vehicle is in automatic driving mode and the lane lines on the road the vehicle is currently traveling on cannot be identified, and determine the target vehicle among the candidate vehicles based on the driving trajectory of the candidate vehicles, so as to further control the vehicle to travel according to the driving trajectory of the target vehicle. Among them, the target vehicle is the vehicle with the highest parallelism among the candidate vehicles, and the parallelism of the target vehicle is the sum of the trajectory parallelism of the target vehicle and other vehicles among the candidate vehicles. The trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of the two vehicles. Therefore, the present application can avoid the interruption or instability of the automatic driving mode caused by the disappearance of lane lines, and improve the adaptability and robustness of the intelligent driving system in various complex road environments.

[0065] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0066] The vehicle control method provided in the embodiment of the present application can be applied to a vehicle. A vehicle can also be referred to as a vehicle, a mobile carrier, an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell vehicle (FCV), an autonomous vehicle, an intelligent and connected vehicle (ICV), a driverless vehicle, etc.

[0067] In the embodiments of the present application, the vehicle may be a sedan, a sport utility vehicle (SUV), a truck, an electric vehicle, a motorcycle, a tricycle, a special vehicle (such as an ambulance, a fire truck, a police car, etc.), an unmanned taxi, an intelligent networked bus, an automatic driving logistics vehicle, an electric truck, etc. In addition, the method is also applicable to various special vehicles, such as agricultural vehicles, mining vehicles, forestry vehicles, airport vehicles, port vehicles, etc. The present application does not impose specific restrictions on this.

[0068] Figure 1 A schematic structural diagram of a vehicle 100 is shown.

[0069] The vehicle 100 may include a vehicle control system 101. The vehicle control system 101 may include a vehicle control device 1011 and a data acquisition device 1012. A communication connection may be established between the vehicle control device 1011 and the data acquisition device 1012.

[0070] The data acquisition device 1012 may be a vehicle-mounted camera or a vehicle-mounted radar, and this application does not impose any specific restrictions on this.

[0071] In practical applications, the vehicle control device 1011 may be communicatively connected to one or more data acquisition devices 1012 .

[0072] For ease of understanding, the present application takes the communication connection between a vehicle control device 1011 and a data acquisition device 1012 as an example for explanation.

[0073] Optional, Figure 1 The vehicle control device 1011 and the data acquisition device 1012 may be functional modules integrated into the same device, or may be two devices independently provided. This application does not impose any limitation on this.

[0074] It is easy to understand that when the vehicle control device 1011 and the data acquisition device 1012 are functional modules integrated in the same device, the communication method between the vehicle control device 1011 and the data acquisition device 1012 is the communication between the internal modules of the device. In this case, the communication process between the two is the same as "the communication process between the vehicle control device 1011 and the data acquisition device 1012 when they are independently set up".

[0075] For ease of understanding, the present application is mainly explained by taking the example that the vehicle control device 1011 and the data acquisition device 1012 are independently configured.

[0076] Figure 1 The vehicle control device 1011 can receive the lane lines on the road where the vehicle 100 is currently traveling sent by the data acquisition device 1012, and determine whether the lane lines can be identified, so as to further determine the target vehicle among the candidate vehicles based on the driving trajectory of the candidate vehicles traveling in the same direction in front of the vehicle based on the driving trajectory of the candidate vehicles when the vehicle is in automatic driving mode and the lane lines on the road where the vehicle is currently traveling cannot be identified, so as to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0077] Optionally, Figure 1 The vehicle control device 1011 and the data acquisition device 1012 may be a terminal, a server, or other types of electronic devices. Figure 1 What is shown is only an example of the device form of the vehicle control device 1011 and the data acquisition device 1012, and does not constitute a limitation thereto.

[0078] In the case where the vehicle control device 1011 and the data acquisition device 1012 are terminals, the terminal can be a device for providing voice and / or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem. The terminal can communicate with one or more core networks via a radio access network (RAN). The terminal can be a mobile terminal, such as a computer with a mobile terminal, or a mobile device built into the vehicle 100, which exchanges language and / or data with a radio access network, such as a mobile phone, a tablet computer, a laptop computer, a netbook, a personal digital assistant (PDA). This application does not impose any restrictions on this.

[0079] When the vehicle control device 1011 and the data acquisition device 1012 are servers, the server may be a single server, or a server cluster consisting of multiple servers. In some implementations, the server cluster may also be a distributed cluster. This application does not impose any restrictions on this.

[0080] It should be noted that the structure illustrated in the embodiment of the present application does not constitute a limitation on the vehicle 100. It may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0081] For ease of understanding, the vehicle control method provided in the present application is specifically introduced below with reference to the accompanying drawings.

[0082] Figure 2 is a flow chart of a vehicle control method according to an exemplary embodiment. Figure 2 As shown, the vehicle control method is applied to a vehicle, and includes the following steps: S201-S203.

[0083] S201. When the vehicle is in an automatic driving mode and the lane line on the road on which the vehicle is currently traveling cannot be identified, obtain a driving trajectory of a candidate vehicle traveling in the same direction as the vehicle in front of the vehicle.

[0084] In the embodiment of the present application, the vehicle may be equipped with a camera and a radar. The radar may be a millimeter wave radar or other types of radar. The present application does not make any specific restrictions on this.

[0085] The camera can collect the lane curve equation, lane type, lane merging point, target information ahead, and passable area ahead of the current lane and adjacent lanes on the road the vehicle is currently traveling on in real time. The radar can obtain vehicle target information and radar reflection point data.

[0086] In one possible implementation, the vehicle control device may determine whether the lane lines on the road the vehicle is currently traveling on are unrecognizable based on data collected in real time by the camera.

[0087] Specifically, when the vehicle is in automatic driving mode (it can also be navigation assisted driving mode, that is, the assisted driving mode has been turned on and the navigation destination has been set), the camera can identify the lane line information and send the collected lane line information to the vehicle control device. The vehicle control device can determine whether the lane line is unrecognizable based on the lane line information.

[0088] Exemplarily, the vehicle control device may determine that the lane line on the road the vehicle is currently traveling on cannot be identified when one or more of the following conditions are met: the lane line is not identified, the lane line length is less than a first length threshold (for example, the lane line length is less than 10 meters, and the first length threshold can be determined based on the actual vehicle speed), the lane line image segmentation quality is low, the lane line coefficient is unreasonable (for example, the lane line lateral deviation is greater than 5 meters, the lane line course angle is greater than 10 degrees, and the lane line radius is less than 25 meters), and the longitudinal distances between the starting points of the left and right lane lines are both greater than a second length threshold (the second length threshold can be 10 meters).

[0089] In addition, when the vehicle is equipped with a high-precision map, the vehicle control device can also confirm the status of the high-precision map. That is, the vehicle control device can determine whether the high-precision map is in a fixed solution state. When the high-precision map is in a fixed solution state, it is determined that the high-precision map is in an available state at this time, and the vehicle control device can select lane lines, lane center lines, road boundaries, lane topology relationships and other information in the high-precision map for path planning. When the high-precision map is not in a fixed solution state, it is determined that the high-precision map is in an unavailable state at this time.

[0090] It can be understood that the fixed solution state is used to indicate the final solution obtained by receiving the differential signal from the base station and solving the carrier phase differential data during the GPS positioning process. In the fixed solution state, the integer ambiguity is fixed to an integer, thereby achieving high-precision positioning. The fixed solution is unique, deterministic and stable, and its accuracy is usually very high, reaching the centimeter level.

[0091] In one possible implementation, when the vehicle is in autonomous driving mode and the lane lines on the road the vehicle is currently traveling on cannot be identified, the vehicle control device can obtain the driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle through a camera and a radar.

[0092] Among them, the candidate vehicle may satisfy one or more of the following: the candidate vehicle is a motor vehicle, the distance between the candidate vehicle and the vehicle is less than a first distance threshold, the heading angle difference between the candidate vehicle and the vehicle is less than a first angle threshold, the speed of the candidate vehicle is greater than a speed threshold, the lateral deviation value between the driving trajectory of the candidate vehicle and the estimated trajectory of the vehicle is less than a deviation threshold, and the difference between the heading angle of the candidate vehicle and the estimated trajectory of the vehicle is less than a second angle threshold.

[0093] Optionally, the first distance threshold may be set according to actual needs. For example, the first distance threshold may be 100 meters or 80 meters. This application does not impose any specific restrictions on this.

[0094] Optionally, the first angle threshold may be set according to actual needs. For example, the first angle threshold may be 10 degrees or 8 degrees. This application does not impose any specific restrictions on this.

[0095] Optionally, the speed threshold can be set according to actual needs. For example, the speed threshold can be 10 kilometers per hour or 15 kilometers per hour. This application does not make specific restrictions on this.

[0096] Optionally, the speed threshold can be set according to actual needs. For example, the speed threshold can be 10 kilometers per hour or 15 kilometers per hour. This application does not make specific restrictions on this.

[0097] Optionally, the deviation threshold can be set according to actual needs. For example, the deviation threshold can be 6 meters or 5 meters. This application does not make specific restrictions on this.

[0098] Optionally, the first angle threshold may be set according to actual needs. For example, the second angle threshold may be 10 degrees or 5 degrees. This application does not impose any specific restrictions on this.

[0099] Specifically, the vehicle control device can collect data on other vehicles on the road the vehicle is currently traveling on through cameras and radars. The vehicle control device can process the data collected by the camera and radar to obtain the longitudinal distance between other vehicles and the vehicle itself (i.e., the vehicle equipped with the vehicle control device in this application), the difference in the heading angle between other vehicles and the vehicle, the speed of other vehicles, the lateral deviation value between other vehicles and the vehicle itself, and the difference between the heading angle of other vehicles and the estimated trajectory of the vehicle, and screen other vehicles based on multiple thresholds to obtain qualified candidate vehicles. The vehicle control device can detect the driving trajectory of the candidate vehicle through the camera and radar to obtain the driving trajectory of the candidate vehicle.

[0100] In another possible implementation, in the process of determining candidate vehicles, the vehicle control device may remove jump targets from other vehicles, that is, eliminate vehicles whose position change from the previous frame is greater than a change threshold.

[0101] Specifically, for newly added targets (i.e., targets that appear in the current frame but have no corresponding targets in the previous frame), the position change detection process should be skipped because these new targets have no previous position information for comparison.

[0102] Secondly, for the target that already exists in the previous frame, its horizontal and vertical position change values ​​need to be calculated, that is, the target position change in the vertical direction (such as along the direction of travel) and the horizontal direction (such as perpendicular to the direction of travel) between the current frame and the previous frame is calculated respectively.

[0103] Then, a judgment is made based on the preset threshold value. If the target's position change in the longitudinal direction exceeds the longitudinal change threshold value, or the position change in the lateral direction exceeds the lateral change threshold value, the target is considered to be a jump target, and the data of the jump target needs to be removed.

[0104] Optionally, the lateral change threshold can be set according to actual needs. For example, the lateral change threshold can be 10 meters or 20 meters. This application does not make specific restrictions on this.

[0105] Optionally, the longitudinal change threshold can be set according to actual needs. For example, the longitudinal change threshold can be 2 meters or 3 meters. This application does not make specific restrictions on this.

[0106] In one possible implementation, the vehicle control device may assign a unique identifier to each candidate vehicle and collect location information of the candidate vehicle in real time based on cameras and radars.

[0107] In order to achieve accurate trajectory recording, the vehicle control device may set a distance parameter (for example, the distance parameter may be 2 meters). The vehicle control device may automatically record a position point whenever the candidate vehicle travels about 2 meters, and use the position point as a trajectory point of the driving trajectory.

[0108] In addition, in order to update the position information of the candidate vehicle in real time, the vehicle control device can adopt a dead reckoning method based on the state of the vehicle. That is, the vehicle control device can perform real-time calculations based on the speed and yaw rate of the vehicle to predict and update the position of the candidate vehicle in the current vehicle coordinate system. When the number of trajectory points of the candidate vehicle exceeds a certain threshold, the vehicle control device can fit multiple trajectory points to obtain the driving trajectory of the candidate vehicle.

[0109] Exemplarily, the dead reckoning method satisfies the following first formula:

[0110]

[0111] Among them, Δlong can be used to represent the distance traveled by the candidate vehicle in period T. Δheading can be used to represent the change value of the yaw angle of the candidate vehicle in period T. ego Can be used to represent the vehicle speed. ego It can be used to characterize the yaw rate of the vehicle. T can be used to characterize the program running cycle. Veh_long can be used to characterize the longitudinal position of the candidate vehicle. Veh_lat can be used to characterize the lateral position of the candidate vehicle.

[0112] The method for fitting multiple trajectory points may be the least square method, and the fitting equation may satisfy the following second formula:

[0113] y=(c0+c1*x1+c2*x1^2+c3*x1^3The second formula

[0114] Among them, y can be used to characterize the position of the vehicle trajectory in the vertical direction (usually the road width direction, but it can also be other vertical reference directions). x1 can be used to characterize the position of the vehicle trajectory in the horizontal direction (usually the road length direction, but it can also be other horizontal reference directions). c0, c1, c2, and c3 are the coefficients of the equation, which are calculated through the least squares fitting process.

[0115] S202: Determine a target vehicle among the candidate vehicles based on the driving trajectories of the candidate vehicles.

[0116] The target vehicle is the vehicle with the highest parallelism among the candidate vehicles. The parallelism of the target vehicle is the sum of the track parallelisms of the target vehicle and other vehicles among the candidate vehicles. Track parallelism is used to characterize the degree of parallelism between the driving tracks of two vehicles.

[0117] In a possible implementation, when the number of candidate vehicles is greater than two, the vehicle control device may determine the track parallelism between every two candidate vehicles based on the driving tracks of the candidate vehicles, and add the track parallelism corresponding to each candidate vehicle to obtain the parallelism corresponding to each candidate vehicle. The vehicle control device may determine the vehicle with the highest parallelism among the candidate vehicles as the target vehicle.

[0118] In addition, when the number of candidate vehicles is less than two, the vehicle control device can calculate the parallelism between the trajectory extended from the vehicle's historical trajectory (the trajectory of the previous frame) and the driving trajectory of the candidate vehicles, and determine the vehicle with the highest parallelism among the candidate vehicles as the target vehicle.

[0119] The parallelism of a trajectory point can be represented by the standard deviation of the projection distance from the point to the trajectory. The trajectory parallelism of two trajectories can be represented by the standard deviation of the projection distance from the trajectory point of one trajectory to the other trajectory.

[0120] Exemplarily, the calculation method / procedure of trajectory parallelism is as follows:

[0121] (for x=startX:1:endX

[0122] delta_lat(x)=Ceof1(x)-Ceof2(x);

[0123] end

[0124] Parall_Values=std(delta_lat) / (endX-startX);)

[0125] Where startX is the starting point of the driving track, and the larger value of the starting points of the two tracks is taken. endX is the end point of the track, and the smaller value of the end points of the two tracks is taken. Ceof is the track equation. std() is the standard deviation equation of the array.

[0126] In another possible implementation, the lateral distance and longitudinal distance between the candidate vehicle and the previous frame trajectory of the vehicle can also be used as a condition for selecting the target vehicle. For example, the vehicle control device can select the vehicle with the smallest value obtained by weighted sum of the lateral distance and the longitudinal distance among the candidate vehicles as the target vehicle.

[0127] Exemplarily, the weighted calculation method is as follows:

[0128] Dis_Value=Max(0,2-Abs(Lat_Dis)) / 2+2*Max(0,100-Long_Dis) / 100 The third formula

[0129] Among them, Lat_Dis is the lateral distance of the candidate vehicle relative to the previous frame trajectory. Long_Dis is the longitudinal distance of the candidate vehicle relative to the previous frame trajectory. Dis_Value can be used to represent the value obtained by weighted sum of lateral distance and longitudinal distance.

[0130] S203: Control the vehicle to travel along the driving trajectory of the target vehicle.

[0131] In one possible manner, the vehicle control device may control the vehicle to move from a current position to a starting point of the driving track of the target vehicle. After controlling the vehicle to move to the starting point, the vehicle control device may control the vehicle to travel according to the driving track of the target vehicle.

[0132] It is understandable that there is a certain longitudinal distance between the self-vehicle and the target vehicle. Before following the target vehicle, the vehicle needs to be driven to the starting point of the target vehicle's driving track, that is, to the first track point in the target vehicle's driving track.

[0133] In order to move the vehicle from the current position to the starting point of the driving trajectory of the target vehicle, the vehicle control device can obtain the current position, heading angle and steering wheel angle of the vehicle. The vehicle control device can determine the predicted driving trajectory from the current position to the starting point based on the current position, heading angle, steering wheel angle and starting point. The vehicle control device can control the vehicle to travel from the current position to the starting point of the driving trajectory of the target vehicle according to the predicted driving trajectory.

[0134] Specifically, starting from the current vehicle position, the vehicle control device can use a specific method to plan a cubic equation until it reaches the starting point of the target vehicle's driving trajectory. The method can be planned based on the following four constraints: the current position of the vehicle, the heading angle of the vehicle, the curvature corresponding to the steering wheel angle of the vehicle, and the starting point of the target vehicle's driving trajectory.

[0135] The planning process can be as follows: First, based on the current position of the vehicle, the heading angle of the vehicle, the curvature corresponding to the steering wheel angle of the vehicle, and the starting point of the target vehicle's driving trajectory, the vehicle control device can construct a cubic equation to represent the trajectory from the current vehicle position to the starting point of the target vehicle's driving trajectory.

[0136] Then, the vehicle control device can discretize the cubic equation to generate a set of points. At the same time, the vehicle control device can discretize the equation of the translation of the driving trajectory of the selected target vehicle to generate another set of points.

[0137] Then, the vehicle control device can use the least square method to fit the two sets of points into a trajectory. This trajectory takes into account both the vehicle's starting position and heading, and also incorporates the shape characteristics of the target trajectory, thereby forming a preliminary planning trajectory.

[0138] Furthermore, if the starting point of the target vehicle's driving trajectory is less than a preset threshold value (for example, the preset threshold value may be the larger value of the vehicle speed and 10 meters) from the current vehicle position, the above planning process will be considered invalid. In this case, the vehicle control device may directly translate the selected driving trajectory to the starting position of the current vehicle, and then fit it into a trajectory using the least squares method.

[0139] In addition, if the vehicle is in the automatic driving mode, the lane line on the road the vehicle is currently traveling on cannot be identified, and no candidate vehicle traveling in the same direction in front of the vehicle is identified, the vehicle control device can obtain the current trajectory information of the vehicle. The vehicle control device can perform trajectory calculation based on the current trajectory information to obtain the calculated trajectory, so as to control the vehicle to travel according to the calculated trajectory.

[0140] based on Figure 2 According to the technical solution in the present application, when the lane lines cannot be identified, the vehicle can maintain a relatively stable driving state by following the candidate vehicle with the highest parallelism, avoiding the risk of deviating from the road or colliding with other vehicles due to unclear lane lines, and avoiding the interruption or instability of the automatic driving mode due to the disappearance of lane lines, thereby improving the adaptability and robustness of the intelligent driving system in various complex road environments.

[0141] In some embodiments, when the vehicle is in automatic driving and the lane lines on the road on which the vehicle is traveling can be identified, the vehicle control method provided by the present application also includes: S301-S303.

[0142] S301. When the vehicle is in automatic driving and lane lines on the road on which the vehicle is traveling can be identified, determine an initial trajectory based on the lane lines.

[0143] Exemplarily, the vehicle control device can determine that the lane line on the road the vehicle is currently traveling on is identifiable when the lane line is identifiable, the lane line length is greater than or equal to a first length threshold, the lane line image segmentation quality is high, the lane line coefficient is reasonable, and the longitudinal distance between the starting points of the left and right lane lines is less than or equal to a second length threshold.

[0144] In one possible implementation, for each valid lane line, the vehicle control device may translate a preset distance (e.g., 1.8 meters) on both sides thereof as a first initial trajectory. Then, the vehicle control device may extend the first initial trajectory along the rear of the vehicle for subsequent safety assessment.

[0145] The vehicle control device can perform a safety assessment on the extended first initial trajectory. If there is a vehicle traveling in parallel with the vehicle, or a dangerous vehicle behind the vehicle whose time to collision (TTC) with the vehicle is less than a preset time (e.g., three seconds), the vehicle control device can remove the first initial trajectory from being a possible driving trajectory of the vehicle.

[0146] After the above screening, for the remaining first initial trajectories that have not been eliminated, the vehicle control device can further determine which first initial trajectory is the optimal trajectory. The vehicle control device can calculate the lateral deviation between the starting point of each first trajectory and the trajectory of the previous frame, and determine the first initial trajectory with the smallest lateral deviation as the second initial trajectory.

[0147] Then, in order to plan a specific driving trajectory, the vehicle control device can select three trajectory points from the trajectory calculated based on the previous frame trajectory. For example, the first trajectory point can be located at the current longitudinal position of the vehicle (ie, 0 meters), the second trajectory point can be located at the position of max (5 meters, 3.6 times the speed) in front of the vehicle, and the third point can be located at max (10 meters, 7.2 times the speed). At the same time, for the second initial trajectory that has been determined, the vehicle control device can also select three trajectory points. For example, the first point is located at the position of max (10 meters, 7.2 times the speed) in front of the vehicle, the second trajectory point is max (15 meters, 10.8 times the speed), and the third point is max (20 meters, 15.2 times the speed). The vehicle control device can plan the initial trajectory based on the six points determined above as the control points of the Bezier curve.

[0148] S302: Determine an obstacle avoidance trajectory based on the initial trajectory and obstacles detected by the vehicle.

[0149] Among them, the obstacle avoidance trajectory can be used to avoid obstacles.

[0150] Optionally, the vehicle control device may use a preset algorithm to determine an obstacle avoidance trajectory based on the initial trajectory and obstacles detected by the vehicle.

[0151] In one example, the vehicle control device may use the A* algorithm to optimize the free space points detected by the radar and the camera to achieve the obstacle avoidance function. The vehicle control device may use the A* algorithm to calculate and generate a series of point sets after obstacle avoidance. The vehicle control device may perform a third-order curve fitting on the point set after obstacle avoidance according to the least squares method to obtain the obstacle avoidance trajectory.

[0152] S303: When the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than a second distance threshold, control the vehicle to travel according to the obstacle avoidance trajectory.

[0153] It is understandable that after completing the fitting of the obstacle avoidance trajectory, the vehicle control device can compare the obstacle avoidance trajectory with the initial trajectory to evaluate the effectiveness of the obstacle avoidance trajectory.

[0154] Specifically, the vehicle control device may calculate a maximum deviation of the obstacle avoidance trajectory relative to the initial trajectory. The vehicle control device may determine that the obstacle avoidance trajectory is valid when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than a second distance threshold, and control the vehicle to travel according to the obstacle avoidance trajectory.

[0155] Alternatively, the vehicle control device may determine that the obstacle avoidance trajectory is wireless and switch the vehicle from the automatic driving mode to the manual driving mode when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is greater than or equal to the second distance threshold.

[0156] Exemplarily, the second distance threshold may be set according to actual needs. For example, the second distance threshold may be 2 meters or 3 meters. This application does not impose any specific restrictions on this.

[0157] In addition, the vehicle control device may perform static obstacle collision detection on the obstacle avoidance trajectory, that is, calculate the lateral distance between all obstacle points and the obstacle avoidance trajectory. The vehicle control device may determine that the obstacle avoidance trajectory has a collision risk when the distance is less than a third distance threshold. The vehicle control device may immediately take emergency action to avoid an accident (for example, switching the vehicle from an automatic driving mode to a manual driving mode) when the ratio between the collision distance and the headway is less than a ratio threshold.

[0158] Based on the above technical solution, the present application can ensure that the vehicle transitions to the target trajectory in a smoother manner by moving to the starting point of the trajectory first, reduce abrupt acceleration changes, improve the riding experience, and avoid when the vehicle starts to move from the current position, directly jumping to the driving trajectory of the target vehicle may produce large acceleration or deceleration changes, which may affect the comfort of passengers.

[0159] In some embodiments, Figure 3 The figure shows a schematic diagram of a vehicle following process.

[0160] In a possible implementation, the vehicle control device may determine whether the lane line cannot be identified. The vehicle control device may determine whether the high-precision map is available when the lane line cannot be identified. The vehicle control device may determine the target vehicle when the high-precision map is not available. The vehicle control device may determine whether the target vehicle is determined. The vehicle control device may follow the trajectory points of the target vehicle when the target vehicle is determined. The vehicle control device may fit the trajectory points of the target vehicle. The vehicle control device may calculate the parallelism of the fitted trajectory. The vehicle control device may calculate the driving trajectory. The vehicle control device may determine whether the lane line is restored. The vehicle control device may output the driving trajectory when the lane line is not restored. The vehicle control device may plan obstacle avoidance based on the driving trajectory to obtain the obstacle avoidance trajectory. The following vehicle trajectory may perform collision detection based on the obstacle avoidance trajectory. The vehicle control device may use the center line of the high-precision map as the driving trajectory when the high-precision map is available. The vehicle control device may perform dead reckoning based on the trajectory of the previous frame when the target vehicle is not determined to obtain the driving trajectory. The vehicle control device may determine the driving trajectory based on the lane line when the lane line is restored.

[0161] In some embodiments, Figure 4 , which is a schematic diagram of an automatic driving application scenario when lane lines cannot be recognized.

[0162] In one possible implementation, Figure 4 Among them, 1 can be used to represent the ego vehicle. 2 can be used to represent the ego vehicle coordinate system. 3 can be used to represent the lane line. 4 can be used to represent the driving trajectory planned in the previous frame (i.e., the estimated trajectory). 5, 6, and 7 can be used to represent candidate vehicles. 7 can be used to represent the target vehicle. 8 can be used to represent non-candidate vehicles. 9 can be used to represent the trajectory points of candidate vehicles. 10 can be used to represent the driving trajectory with the highest parallelism (i.e., the target trajectory). 11 can be used to represent the trajectory starting point of the target trajectory. 12 can be used to represent the driving trajectory from the ego vehicle position to the track starting point. 13 can be used to represent freespace obstacle points.

[0163] In some embodiments, in combination Figure 4 ,like Figure 5 , which is a schematic diagram of an automatic driving application scenario when lane lines can be recognized.

[0164] In one possible implementation, Figure 5 Among them, 1 can be used to represent the ego vehicle. 3 can be used to represent the lane line. 6 and 7 can be used to represent the candidate vehicles. 7 can be used to represent the target vehicle. 9 can be used to represent the trajectory point of the candidate vehicle. 10 can be used to represent the driving trajectory with the highest parallelism (i.e., the target trajectory). 11 can be used to represent the trajectory starting point of the target trajectory. 14 can be used to represent the driving trajectory obtained after translation according to the newly appeared lane line. 15 can be used to represent the driving trajectory planned according to the lane line.

[0165] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the vehicle control device or electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0166] The embodiments of the present application can exemplarily divide the functional modules of the vehicle control device or electronic device according to the above method. For example, the vehicle control device or electronic device can include various functional modules corresponding to the various functional divisions, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical functional division. There may be other division methods in actual implementation.

[0167] Figure 6 is a block diagram of a vehicle control device according to an exemplary embodiment. Figure 6 The vehicle control device includes: an acquisition unit 401, a determination unit 402 and a control unit 403.

[0168] In one possible manner, the acquisition unit 401 is used to acquire the driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle when the vehicle is in an automatic driving mode and the lane lines on the road on which the vehicle is currently traveling cannot be identified.

[0169] In a possible manner, the determination unit 402 is configured to determine a target vehicle among the candidate vehicles based on the driving trajectories of the candidate vehicles.

[0170] In one possible manner, the control unit 403 is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

[0171] In one possible manner, the control unit 403 is specifically used to: control the vehicle to move from the current position to the starting point of the driving trajectory of the target vehicle; after controlling the vehicle to move to the starting point, control the vehicle to travel according to the driving trajectory of the target vehicle.

[0172] In one possible manner, the control unit 403 is specifically used to: obtain the current position, heading angle and steering wheel angle of the vehicle; determine the predicted driving trajectory from the current position to the starting point based on the current position, heading angle, steering wheel angle and starting point; control the vehicle to travel from the current position to the starting point according to the predicted driving trajectory.

[0173] In one possible manner, the control unit 403 is specifically used to: determine multiple trajectory points from the current position to the starting point based on the current position, heading angle, and steering wheel angle; and fit the multiple trajectory points to obtain a predicted driving trajectory.

[0174] In one possible manner, the acquisition unit 401 is also used to obtain the vehicle's current trajectory information when the vehicle is in automatic driving mode, the lane lines on the road the vehicle is currently traveling on cannot be identified, and no candidate vehicle traveling in the same direction in front of the vehicle is identified.

[0175] In a possible manner, the determination unit 402 is further configured to perform trajectory estimation based on the current trajectory information to obtain an estimated trajectory.

[0176] In one possible embodiment, the control unit 403 is also used to control the vehicle to travel according to the calculated trajectory.

[0177] In one possible manner, the determination unit 402 is further configured to determine the initial trajectory based on the lane lines when the vehicle is in automatic driving and the lane lines on the road on which the vehicle is traveling can be identified.

[0178] In a possible manner, the determination unit 402 is further configured to determine an obstacle avoidance trajectory based on the initial trajectory and the obstacle detected by the vehicle; the obstacle avoidance trajectory is used to avoid the obstacle.

[0179] In a possible manner, the control unit 403 is further configured to control the vehicle to travel according to the obstacle avoidance trajectory when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than the second distance threshold.

[0180] In one possible manner, the control unit 403 is further configured to switch the vehicle from the automatic driving mode to the manual driving mode when the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is greater than or equal to the second distance threshold.

[0181] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0182] Figure 7 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device includes but is not limited to: a processor 501 and a memory 502 .

[0183] The memory 502 is used to store executable instructions of the processor 501. It can be understood that the processor 501 is configured to execute instructions to implement the vehicle control method in the above embodiment.

[0184] It should be noted that those skilled in the art can understand that Figure 7 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include Figure 7 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0185] The processor 501 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 502, and calling data stored in the memory 502, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 501 may include one or more processing units. Optionally, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

[0186] The memory 502 can be used to store software programs and various data. The memory 502 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application program required by at least one functional module (such as a determination unit, a processing unit, etc.), etc. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0187] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 502 including instructions. The above instructions can be executed by a processor 501 of an electronic device to implement the method in the above embodiment.

[0188] In actual implementation, Figure 6 The functions of the acquisition unit 401, the determination unit 402, and the control unit 403 in Figure 7 The processor 501 in the embodiment calls the computer program stored in the memory 502. The specific execution process can refer to the description of the method part in the above embodiment, which will not be repeated here.

[0189] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0190] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, and the one or more instructions can be executed by the processor 501 of the electronic device to complete the method in the above embodiment.

[0191] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0192] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0193] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0194] The units described as separate components may or may not be physically separated, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0195] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0196] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, ROM, RAM, disk or CD and other media that can store program code.

[0197] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto, and any changes or substitutions within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: include: When the vehicle is in an automatic driving mode and a lane line on a road on which the vehicle is currently traveling cannot be identified, obtaining a driving trajectory of a candidate vehicle traveling in the same direction in front of the vehicle; Determining a target vehicle among the candidate vehicles based on the driving trajectories of the candidate vehicles; The target vehicle is the vehicle with the highest parallelism among the candidate vehicles; The parallelism of the target vehicle is the sum of the parallelism of the trajectories of the target vehicle and other vehicles among the candidate vehicles; The track parallelism is used to characterize the parallelism between the driving tracks of two vehicles; Control the vehicle to travel along the travel trajectory of the target vehicle.

2. The method according to claim 1, characterized in that The candidate vehicle meets one or more of the following requirements: The distance between the vehicle and the vehicle is less than a first distance threshold; The difference in orientation angle between the vehicle and the vehicle is less than a first angle threshold; The speed is greater than the speed threshold; The lateral deviation value from the estimated trajectory of the vehicle is less than a deviation threshold.

3. The method according to claim 1 or 2, characterized in that: The controlling the vehicle to travel according to the travel trajectory of the target vehicle includes: Controlling the vehicle to move from a current position to a starting point of a driving trajectory of the target vehicle; After controlling the vehicle to move to the starting point, controlling the vehicle to travel according to the driving track of the target vehicle.

4. The method according to claim 3, characterized in that: The controlling the vehicle to move from the current position to the starting point of the driving trajectory of the target vehicle includes: Obtaining the current position, heading angle and steering wheel angle of the vehicle; Determining a predicted driving trajectory from the current position to the starting point based on the current position, the heading angle, the steering wheel angle, and the starting point; The vehicle is controlled to travel from the current position to the starting point according to the predicted driving trajectory.

5. The method according to claim 4, characterized in that The step of determining a predicted driving trajectory from the current position to the starting point based on the current position, the heading angle, the steering wheel angle, and the starting point includes: Based on the current position, the heading angle, and the steering wheel angle, determining a plurality of trajectory points from the current position to the starting point; The multiple trajectory points are fitted to obtain the predicted driving trajectory.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: When the vehicle is in an automatic driving mode, a lane line on a road on which the vehicle is currently traveling cannot be identified, and no candidate vehicle traveling in the same direction in front of the vehicle is identified, obtaining current trajectory information of the vehicle; Perform trajectory extrapolation based on the current trajectory information to obtain an extrapolated trajectory; The vehicle is controlled to travel according to the estimated trajectory.

7. The method according to claim 6, characterized in that The method further comprises: When the vehicle is in automatic driving and lane lines on a road on which the vehicle is traveling can be identified, determining an initial trajectory based on the lane lines; Determining an obstacle avoidance trajectory based on the initial trajectory and the obstacle detected by the vehicle; the obstacle avoidance trajectory is used to avoid the obstacle; When the maximum deviation between the initial trajectory and the obstacle avoidance trajectory is less than a second distance threshold, the vehicle is controlled to travel according to the obstacle avoidance trajectory.

8. The method according to claim 7, characterized in that The method further comprises: When a maximum deviation between an initial trajectory and the obstacle avoidance trajectory is greater than or equal to a second distance threshold, the vehicle is switched from the automatic driving mode to a manual driving mode.

9. A vehicle control device, characterized in that: The device comprises: an acquisition unit, a determination unit and a control unit; The acquisition unit is used to acquire the driving trajectory of the candidate vehicle traveling in the same direction ahead of the vehicle when the vehicle is in the automatic driving mode and the lane line on the road the vehicle is currently traveling on cannot be identified; The determination unit is used to determine a target vehicle among the candidate vehicles based on the driving trajectories of the candidate vehicles; the target vehicle is the vehicle with the highest parallelism among the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelisms of the target vehicle and other vehicles among the candidate vehicles; the trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of two vehicles; The control unit is used to control the vehicle to travel according to the driving trajectory of the target vehicle.

10. A vehicle control system, characterized in that: The system comprises: a data acquisition device and a vehicle control device; The data acquisition device is used to collect the driving trajectory of the candidate vehicle traveling in the same direction ahead of the vehicle when the vehicle is in the automatic driving mode and the lane line on the road the vehicle is currently traveling on cannot be identified; The vehicle control device is used to determine a target vehicle among the candidate vehicles based on the driving trajectories of the candidate vehicles collected by the data collection device; the target vehicle is the vehicle with the highest parallelism among the candidate vehicles; the parallelism of the target vehicle is the sum of the trajectory parallelisms of the target vehicle and other vehicles among the candidate vehicles; the trajectory parallelism is used to characterize the degree of parallelism between the driving trajectories of two vehicles; The vehicle control device is also used to control the vehicle to travel according to the driving trajectory of the target vehicle.

11. A vehicle, characterized in that: The vehicle comprises the system of claim 10.

12. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 8.

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