Vehicle control method and system for mountain road agglomerate fog scene

Abstract

The invention discloses a mountain road agglomerate fog scene-oriented vehicle control method and system, and the method comprises the steps: obtaining the front obstacle information through sensing the obstacle condition of a road section in front of a vehicle based on the current visibility grade; acquiring waypoint information of a road section in front of the vehicle according to the current position of the vehicle; according to the front obstacle information, the waypoint information and the collected user response time, a preset agglomerate fog coping strategy matrix is adjusted, and a vehicle control strategy is generated; and adjusting a driving path of the vehicle in the agglomerate fog scene according to the vehicle control strategy. According to the driving habit of the user and the current driving environment, the vehicle control strategy coping with the agglomerate fog scene is generated, and the driving safety and the user experience in foggy days are improved.

Description

Technical Field

[0001] The present application belongs to the field of intelligent driving technology, and specifically relates to a vehicle control method and system for mountain road fog scenes. Background Art

[0002] Existing intelligent driving systems are mainly based on strong visual perception solutions, equipped with millimeter-wave radar to assist in judging obstacles ahead. However, whether it is autonomous driving by the driver or intelligent driving's lidar or strong visual solutions, when dealing with foggy scenes, there will be boundary scenes that cannot be coped with.

[0003] When mountain fog appears on mountain roads, the intelligent driving system usually recognizes that the camera is blocked, and then quickly enters the user takeover reminder mode, and the system begins to prepare to exit. If the fog is relatively large, the system will exit directly and be handed over to the driver for operation. In this extreme case, the user is given less than 5 seconds to respond, which poses a greater safety risk in high-speed driving scenarios and provides a poor user experience. In order to ensure smoother degradation and control in fog scenes, it is necessary to develop a vehicle control strategy for mountain road fog scenes that can enhance user experience and ensure driving safety. Summary of the invention

[0004] This application proposes a vehicle control method and system for mountain road fog scenes, which generates a vehicle control strategy for fog scenes based on the user's driving habits and the current driving environment, thereby improving driving safety and user experience in foggy weather.

[0005] A first aspect of the present application provides a vehicle control method for a mountain road fog scene, the method comprising:

[0006] Based on the current visibility level, the obstacle information ahead is obtained by sensing the obstacle situation on the road ahead of the vehicle;

[0007] According to the current position of the vehicle, obtain the waypoint information of the road section ahead of the vehicle;

[0008] According to the front obstacle information, the path point information and the collected user response time, a preset fog group response strategy matrix is ​​adjusted to generate a vehicle control strategy;

[0009] According to the vehicle control strategy, adjust the vehicle's driving path in fog scenes.

[0010] The above scheme first senses obstacles in the road ahead of the vehicle according to the current driving environment, obtains the location information of the obstacles in front of the vehicle, and provides data support to avoid collisions due to the inability to see obstacles clearly in a foggy environment; then collects the path point information of the road ahead of the vehicle, and provides data support for the path planning after the vehicle enters the foggy scene; then determines the speed of user reaction through the collected user response time, and adjusts the foggy response strategy matrix to generate a vehicle control strategy that matches the user's driving style, avoiding extreme situations such as switching the vehicle to autonomous driving mode before the user reacts, thereby improving driving safety in foggy scenes. Finally, based on the vehicle control strategy, a safer driving path in foggy scenes with better user experience is formulated to ensure that the vehicle can drive safely in foggy scenes.

[0011] In a possible implementation method of the first aspect, based on the current visibility level, the obstacle situation on the road section ahead of the vehicle is sensed to obtain the ahead obstacle information, specifically:

[0012] According to the current visibility level, determine the current fog level, and set the radar sensing priority according to the fog level;

[0013] Based on the radar sensing priority, obstacles on the road ahead of the vehicle are visually perceived to obtain information about the obstacles ahead.

[0014] The above scheme determines which device should be used to perceive the obstacle information ahead according to the current visibility, ensuring that clearer data can be obtained.

[0015] In a possible implementation method of the first aspect, based on the radar sensing priority, visually perceive obstacles on a road section ahead of the vehicle to obtain ahead obstacle information, specifically:

[0016] According to the radar sensing priority, switching to a corresponding sensing device;

[0017] The distance information and speed information of obstacles on the road section ahead of the vehicle are collected by the sensing device to obtain the obstacle information ahead.

[0018] In a possible implementation method of the first aspect, obtaining the path point information of the road section ahead of the vehicle according to the current position of the vehicle is specifically as follows:

[0019] According to the preset navigation map lane path map and the current position of the vehicle, the weather information of the road ahead of the vehicle in the preset future time period and the relevant path points in the fog scene are obtained;

[0020] The weather information and the relevant path points are integrated to obtain the path point information of the road section ahead of the vehicle.

[0021] The above scheme obtains the road section information and corresponding weather information in the future period based on the lane path map of the navigation map, providing data support for subsequent path planning in foggy scenes.

[0022] In a possible implementation method of the first aspect, it also includes: updating the navigation map lane path map at a preset frequency.

[0023] In a possible implementation method of the first aspect, a preset fog coping strategy matrix is ​​adjusted according to the front obstacle information, the path point information, and the collected user response time to generate a vehicle control strategy, specifically:

[0024] The user response time is collected at a preset time interval, and if the user response time does not exceed a first threshold, the front obstacle information and the path point information are processed according to the group fog coping strategy matrix to generate a vehicle control strategy;

[0025] If the user response time exceeds a first threshold, the response value of the fog group response strategy matrix is ​​adjusted according to the user response time, and the front obstacle information and the path point information are processed by the adjusted fog group response strategy matrix to generate a vehicle control strategy.

[0026] The above scheme determines the speed of user reaction according to the user response time. When the user reacts quickly, there is no need to adjust the group fog response strategy matrix; however, when the user reacts slowly, the response value of the group fog response strategy matrix needs to be adjusted according to the user response time, so that the vehicle control strategy generated by the group fog response strategy matrix gives the user enough reaction time to switch to the user autonomous driving mode, thereby improving the user's driving experience.

[0027] In a possible implementation method of the first aspect, the response value of the fog group response strategy matrix is ​​adjusted according to the user response time, specifically:

[0028] Optimizing the response value according to the user response time; wherein the response value is related to the vehicle chassis execution parameter;

[0029] The time point at which the vehicle switches to the user autonomous driving mode can be determined by the vehicle chassis execution parameters.

[0030] In a possible implementation method of the first aspect, the method further includes:

[0031] When it is recognized that the vehicle leaves the fog scene, the fog coping strategy matrix is ​​initialized;

[0032] When it is identified that a vehicle enters a foggy scene, the initialized foggy coping strategy matrix is ​​adjusted according to the user response time.

[0033] In a possible implementation method of the first aspect, a driving path of a vehicle in a foggy scene is adjusted according to a vehicle control strategy, specifically:

[0034] Determine the driving distance between the vehicle and the obstacle on the road ahead according to the vehicle control strategy, and select a first path point from the path point information;

[0035] The vehicle is driven at the driving distance, and when the vehicle passes the first path point, the intelligent driving system of the vehicle is exited and the vehicle is switched to a user autonomous driving mode.

[0036] The above scheme determines the driving distance between the vehicle and the obstacle according to the vehicle control strategy, ensuring that the vehicle will not collide with the obstacle in the fog scene; at the same time, the first path point is selected from the path point information, and the first path point is used as the timing point to exit the vehicle's intelligent driving system and switch the vehicle to the user's autonomous driving mode, providing the user with sufficient reaction time.

[0037] The second aspect of the present application provides a vehicle control system for mountain road fog scenes, the system comprising: an obstacle information collection module, a path point information collection module, a vehicle control strategy generation module and a vehicle driving control module;

[0038] The obstacle information acquisition module is used to obtain the obstacle information ahead by sensing the obstacle situation on the road ahead of the vehicle based on the current visibility level;

[0039] The waypoint information acquisition module is used to obtain the waypoint information of the road section ahead of the vehicle according to the current position of the vehicle;

[0040] The vehicle control strategy generation module is used to adjust the preset group fog response strategy matrix according to the front obstacle information, the path point information and the collected user response time, and generate a vehicle control strategy;

[0041] The vehicle driving control module is used to adjust the vehicle's driving path in fog scenes according to the vehicle control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a specific flow chart of a vehicle control method for mountain road fog scenes provided by a certain embodiment of the present application;

[0044] Figure 2 This is a system architecture diagram of a vehicle control method for mountain road fog scenes provided by a certain embodiment of the present application;

[0045] Figure 3 This is a typical scenario diagram of a vehicle control method for a mountain road fog scene provided by a certain embodiment of the present application;

[0046] Figure 4 It is a specific structural diagram of a vehicle control system for mountain road fog scenes provided by a certain embodiment of the present application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0049] First embodiment

[0050] When a vehicle is driving on a mountain road, it often encounters mountain fog. The existing technology responds to fog by directly exiting the intelligent driving system and then reminding the driver that the vehicle will enter autonomous driving mode and the driver will control the vehicle. Because different drivers have different driving styles, if the intelligent driving system is exited at the same time interval, drivers with slow reactions or inattention will not be able to adjust their driving posture in time, which will bring greater safety risks in high-speed driving scenarios and give users a poor driving experience.

[0051] Therefore, how to combine weather information and user's driving habits to control the driving state of the vehicle in mountain road fog scenes to improve user driving experience and driving safety is the main research direction of the embodiments of this application.

[0052] like Figure 1 As shown, Figure 1 A specific flow chart of a vehicle control method for a mountain road fog scene is provided for a certain embodiment of the present application. The vehicle control method for a mountain road fog scene of this embodiment includes steps S1 to S4, which are described in detail as follows:

[0053] Step S1, based on the current visibility level, obtains the obstacle information ahead by sensing the obstacle situation on the road ahead of the vehicle.

[0054] In an embodiment of the present application, the vehicle's visual system is first used to identify the fog level of the current environment. Specifically, visibility is first sensed by sensors to determine the current fog level. Then, the radar sensing priority is set according to the fog level, and appropriate equipment is selected to sense obstacles on the road ahead of the vehicle.

[0055] For example, when the vehicle is in a foggy environment with a visibility of 150 meters, a millimeter-wave radar with better penetration but poorer precision and accuracy can be used for perception; in other cases, a camera with better precision and accuracy can also be used for perception.

[0056] The distance and speed information of obstacles on the road ahead are collected through millimeter-wave radar and other equipment to obtain the obstacle information ahead.

[0057] Exemplarily, the obstacle may be a roadblock sign ahead and a vehicle ahead, etc.

[0058] Furthermore, the embodiment of the present application identifies the fog level and switches to the corresponding perception device based on a trained visual perception model. The visual perception model is formed by collating and summarizing various pictures of fog scenes defined by traffic police and corresponding data collected on site, and conducting targeted visual perception algorithm training, so that the model can initially have the ability to identify fog.

[0059] Step S2, obtaining the path point information of the road section ahead of the vehicle according to the current position of the vehicle.

[0060] In an embodiment of the present application, a navigation map lane path map is first obtained online from the Internet, and then weather information about the road section ahead of the vehicle in a certain period of time in the future and related path points in fog scenes are obtained from the navigation map lane path map based on the current position of the vehicle, so as to plan the path for the vehicle in the future period of time.

[0061] The weather information and the relevant path points are then integrated to obtain the path point information of the road section ahead of the vehicle.

[0062] In order to ensure the real-time and accuracy of the waypoint information, it is set to regularly update the vehicle's navigation map lane path map.

[0063] Step S3, adjusting the preset fog coping strategy matrix according to the front obstacle information, the path point information and the collected user response time, and generating a vehicle control strategy.

[0064] In the embodiment of the present application, when the vehicle is in a foggy scene, the user's response time is collected through the user's related actions to determine the user's driving style. Then, a vehicle control strategy that is closer to the user experience requirements is provided according to the user's driving style.

[0065] Specifically, when the vehicle is in a fog scene and begins to exit the intelligent driving system, if the user can respond to the autonomous driving mode within 2 seconds, then the user has a quick reaction; otherwise, it means that the user has a slow reaction and it is necessary to generate a vehicle control strategy that switches the vehicle's driving state more slowly to provide the user with sufficient reaction time to adjust his or her driving operation.

[0066] In the embodiment of the present application, the vehicle control strategy is generated based on a small parameter AI model with self-learning ability. This AI model initially has a trained fog coping strategy matrix. If the user response time does not exceed the set threshold, it means that the user's reaction speed is fast and there is no need to adjust the matrix. The front obstacle information and the path point information are directly processed according to the fog coping strategy matrix to generate a vehicle control strategy.

[0067] If the user response time exceeds the set threshold, it means that the vehicle control strategy generated by the current fog group response strategy matrix is ​​too abrupt for the user, and the response value of the fog group response strategy matrix needs to be adjusted to optimize the vehicle control strategy to adapt to the user's driving style.

[0068] Specifically, if the user responds in time, the response value of the fog group response strategy matrix is ​​adjusted unsupervisedly through the trained small parameter AI model, including but not limited to the vehicle chassis execution parameters. The vehicle chassis execution parameters can be used to determine the time point and speed at which the vehicle switches to the user's autonomous driving mode, providing more response time for users with inattentiveness.

[0069] The fog response strategy matrix is ​​mainly based on the information of obstacles ahead and path points, clarifying the number of obstacles in the road section within the fog scene, the specific location of the obstacles, the distance between the obstacles and the vehicle, and the path point at which the road section turns, etc. Combined with the user response time, it is best to exit the vehicle's intelligent driving system at the location and switch the vehicle to user autonomous driving mode, so that the user can concentrate on autonomous driving the vehicle within the appropriate time and pass the fog section safely.

[0070] After the optimized vehicle control strategy is completed, the front obstacle information and the path point information are processed by the optimized vehicle control strategy to generate a more humane vehicle control strategy.

[0071] Furthermore, in order to make the fog group response strategy matrix meet the needs of different users, when it is recognized that the driver in the driving seat changes or the vehicle enters the fog group scene after a long interval, the fog group response strategy matrix will be initialized, and the user response time will be collected again when the vehicle re-enters the fog group scene to adjust the initialized fog group response strategy matrix.

[0072] In order to better demonstrate how the vehicle control strategy is generated in the embodiment of the present application, Figure 2 A decision system architecture diagram is provided. As shown in the figure, the embodiment of the present application is mainly divided into four modules, namely: a fusion perception module, a map information module, a comprehensive decision module and a core planning decision module. Among them, the fusion perception module collects the obstacle conditions of the road section ahead of the vehicle through the perception device, generates the obstacle information ahead, and sends the obstacle information ahead and the current fog level to the comprehensive decision module; the map information module obtains the path point information and weather information of the road section ahead of the vehicle from the navigation map platform and sends this information to the comprehensive decision module; after receiving the information provided by the fusion perception module and the map information module, the comprehensive decision module adopts the response strategy matrix and combines the driver's response information to generate a vehicle control strategy that conforms to the driver's style, and transmits the vehicle control strategy to the core planning decision module, so as to realize the switching of the vehicle driving mode through the vehicle control strategy.

[0073] Step S4, adjusting the driving path of the vehicle in the fog scene according to the vehicle control strategy.

[0074] In an embodiment of the present application, the driving distance between the vehicle and each obstacle on the road ahead is determined according to the vehicle control strategy, and it is ensured that a safe distance is maintained between the vehicle and each obstacle during subsequent driving to avoid collision.

[0075] At the same time, a first path point can be selected from the path point information according to the vehicle control strategy. When the vehicle passes the first path point, the vehicle's intelligent driving system is exited and the vehicle is switched to user autonomous driving mode to ensure that the user has enough time to take over the vehicle.

[0076] In order to explain in more detail how the embodiments of the present application provide safe and humanized driving services to the driver through the vehicle control strategy, Figure 3 A typical scenario schematic diagram is provided to show how the vehicle completes the driving state with the help of the vehicle control strategy in a typical scenario.

[0077] As shown in the figure, there are two scenes, the left side is the situation where there is no vehicle ahead, and the right side is the situation where there is a vehicle ahead. The road section in the cloud shape in the figure is the road section with a fog scene, point W is the path point in the fog scene, and vehicle A is the vehicle the user is driving. When the perception system of vehicle A senses that there is fog near point W, it is sensed and confirmed by the map weather information module that the fog type of the current road section is heavy fog. At this time, vehicle A activates the navigation map lane path map and the relevant path point information in the fog scene to control the vehicle. The specific control process is as follows:

[0078] (1) When there is no vehicle in front of vehicle A, there is no need to collect obstacle information on the road ahead of the vehicle, and the vehicle control strategy is generated directly through the preset fog response strategy matrix. If the response value does not need to be adjusted, then before passing point W, vehicle A will automatically exit the intelligent driving system and switch to user autonomous driving mode, leaving vehicle A completely under the control of the driver; if the response value is adjusted, the intelligent driving state will continue for a period of time after vehicle A passes point W, and then it will actually switch to user autonomous driving mode.

[0079] (2) When there is a vehicle in front of vehicle A, vehicle A has previously identified vehicle B through the sensing device and followed vehicle B into the fog range. At this time, vehicle B is still visible in the millimeter-wave radar perception, but it is blurred in the camera image. Based on this situation, the current fog type is determined and the vehicle control strategy generation mode is entered. Vehicle A will generate a vehicle control strategy based on the path point information and millimeter-wave radar perception information, maintaining a certain safe distance from vehicle B in front, and referring to keeping the lane stable while safely passing through the foggy section with the assistance of the path points, and reminding the driver to take over at the appropriate time.

[0080] Through the above method, the takeover experience needs of different users in mountain road and highway fog scenes can be met, and the safety performance of the system can be further improved.

[0081] Implementing the embodiments of the present application has the following beneficial effects:

[0082] First, according to the current driving environment, the obstacle perception is performed on the road ahead of the vehicle to obtain the location information of the obstacle in front of the vehicle, so as to provide data support to avoid collisions due to the inability to see the obstacle clearly in the foggy environment; then the path point information of the road ahead of the vehicle is collected to provide data support for the path planning after the vehicle enters the foggy scene; then the user response time is collected to determine the speed of the user's reaction, so as to adjust the foggy response strategy matrix to generate a vehicle control strategy that matches the user's driving style, avoiding extreme situations such as switching the vehicle to autonomous driving mode before the user reacts, thereby improving driving safety in foggy scenes. Finally, based on the vehicle control strategy, a safer driving path with better user experience in foggy scenes is formulated to ensure that the vehicle can drive safely in foggy scenes.

[0083] Second embodiment

[0084] Furthermore, in order to execute the vehicle control system for mountain road fog scene corresponding to the above method embodiment to achieve corresponding functions and technical effects, Figure 4A structural diagram of a vehicle control system for mountain road fog scenes is provided. For ease of description, only the parts related to this embodiment are shown. The vehicle control system for mountain road fog scenes provided by the embodiment of the present application includes:

[0085] The obstacle information collection module 201 is used to obtain the obstacle information ahead by sensing the obstacle situation on the road ahead of the vehicle based on the current visibility level.

[0086] In an embodiment of the present application, the current fog level is determined according to the current visibility level, and the radar sensing priority is set according to the fog level.

[0087] Then, based on the radar sensing priority, the obstacles on the road ahead of the vehicle are visually perceived to obtain the obstacle information ahead.

[0088] The waypoint information acquisition module 202 is used to obtain the waypoint information of the road section ahead of the vehicle according to the current position of the vehicle.

[0089] In an embodiment of the present application, a navigation map lane path map is first obtained online from the Internet, and then weather information about the road section ahead of the vehicle in a certain period of time in the future and related path points in fog scenes are obtained from the navigation map lane path map based on the current position of the vehicle, so as to plan the path for the vehicle in the future period of time.

[0090] The weather information and the relevant path points are then integrated to obtain the path point information of the road section ahead of the vehicle.

[0091] In order to ensure the real-time and accuracy of the waypoint information, it is set to regularly update the vehicle's navigation map lane path map.

[0092] The vehicle control strategy generation module 203 is used to adjust the preset fog coping strategy matrix according to the front obstacle information, the path point information and the collected user response time, and generate a vehicle control strategy.

[0093] In an embodiment of the present application, the user response time is collected at a preset time interval, and if the user response time does not exceed a first threshold, the front obstacle information and the path point information are processed according to the group fog coping strategy matrix to generate a vehicle control strategy;

[0094] If the user response time exceeds a first threshold, the response value of the fog group response strategy matrix is ​​adjusted according to the user response time, and the front obstacle information and the path point information are processed by the adjusted fog group response strategy matrix to generate a vehicle control strategy.

[0095] The vehicle driving control module 204 is used to adjust the driving path of the vehicle in the fog scene according to the vehicle control strategy.

[0096] In an embodiment of the present application, the driving distance between the vehicle and each obstacle on the road ahead is determined according to the vehicle control strategy, and it is ensured that a safe distance is maintained between the vehicle and each obstacle during subsequent driving to avoid collision.

[0097] At the same time, a first path point can be selected from the path point information according to the vehicle control strategy. When the vehicle passes the first path point, the vehicle's intelligent driving system is exited and the vehicle is switched to user autonomous driving mode to ensure that the user has enough time to take over the vehicle.

[0098] In some embodiments, the obstacle information collection module 201 further includes:

[0099] First, the vehicle's visual system identifies the fog level of the current environment. Specifically, the visibility is sensed by the sensor to determine the current fog level. Then, the radar sensing priority is set according to the fog level, and the appropriate equipment is selected to sense obstacles on the road ahead of the vehicle.

[0100] For example, when the vehicle is in a foggy environment with a visibility of 150 meters, a millimeter-wave radar with better penetration but poorer precision and accuracy can be used for perception; in other cases, a camera with better precision and accuracy can also be used for perception.

[0101] The distance and speed information of obstacles on the road ahead are collected through millimeter-wave radar and other equipment to obtain the obstacle information ahead.

[0102] Exemplarily, the obstacle may be a roadblock sign ahead and a vehicle ahead, etc.

[0103] Furthermore, the embodiment of the present application identifies the fog level and switches to the corresponding perception device based on a trained visual perception model. The visual perception model is formed by collating and summarizing various pictures of fog scenes defined by traffic police and corresponding data collected on site, and conducting targeted visual perception algorithm training, so that the model can initially have the ability to identify fog.

[0104] In some embodiments, the vehicle control strategy generation module 203 further includes:

[0105] When the vehicle is in a foggy scene, the user's response time will be collected through the user's related actions to determine the user's driving style. Then, a vehicle control strategy that is closer to the user experience needs is provided based on the user's driving style.

[0106] Specifically, when the vehicle is in a fog scene and begins to exit the intelligent driving system, if the user can respond to the autonomous driving mode within 2 seconds, then the user has a quick reaction; otherwise, it means that the user has a slow reaction and it is necessary to generate a vehicle control strategy that switches the vehicle's driving state more slowly to provide the user with sufficient reaction time to adjust his or her driving operation.

[0107] In the embodiment of the present application, the vehicle control strategy is generated based on a small parameter AI model with self-learning ability. This AI model initially has a trained fog coping strategy matrix. If the user response time does not exceed the set threshold, it means that the user's reaction speed is fast and there is no need to adjust the matrix. The front obstacle information and the path point information are directly processed according to the fog coping strategy matrix to generate a vehicle control strategy.

[0108] If the user response time exceeds the set threshold, it means that the vehicle control strategy generated by the current fog group response strategy matrix is ​​too abrupt for the user, and the response value of the fog group response strategy matrix needs to be adjusted to optimize the vehicle control strategy to adapt to the user's driving style.

[0109] Specifically, if the user responds in time, the response value of the fog group response strategy matrix is ​​adjusted unsupervisedly through the trained small parameter AI model, including but not limited to the vehicle chassis execution parameters. The vehicle chassis execution parameters can be used to determine the time point and speed at which the vehicle switches to the user's autonomous driving mode, providing more response time for users with inattentiveness.

[0110] The fog response strategy matrix is ​​mainly based on the information of obstacles ahead and path points, clarifying the number of obstacles in the road section within the fog scene, the specific location of the obstacles, the distance between the obstacles and the vehicle, and the path point at which the road section turns, etc. Combined with the user response time, it is best to exit the vehicle's intelligent driving system at the location and switch the vehicle to user autonomous driving mode, so that the user can concentrate on autonomous driving the vehicle within the appropriate time and pass the fog section safely.

[0111] After the optimized vehicle control strategy is completed, the front obstacle information and the path point information are processed by the optimized vehicle control strategy to generate a more humane vehicle control strategy.

[0112] Furthermore, in order to make the fog group response strategy matrix meet the needs of different users, when it is recognized that the driver in the driving seat changes or the vehicle enters the fog group scene after a long interval, the fog group response strategy matrix will be initialized, and the user response time will be collected again when the vehicle re-enters the fog group scene to adjust the initialized fog group response strategy matrix.

[0113] Implementing the embodiments of the present application has the following beneficial effects:

[0114] First, according to the current driving environment, the obstacle perception is performed on the road ahead of the vehicle to obtain the location information of the obstacle in front of the vehicle, so as to provide data support to avoid collisions due to the inability to see the obstacle clearly in the foggy environment; then the path point information of the road ahead of the vehicle is collected to provide data support for the path planning after the vehicle enters the foggy scene; then the user response time is collected to determine the speed of the user's reaction, so as to adjust the foggy response strategy matrix to generate a vehicle control strategy that matches the user's driving style, avoiding extreme situations such as switching the vehicle to autonomous driving mode before the user reacts, thereby improving driving safety in foggy scenes. Finally, based on the vehicle control strategy, a safer driving path with better user experience in foggy scenes is formulated to ensure that the vehicle can drive safely in foggy scenes.

[0115] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A vehicle control method for mountain road fog scenes, characterized in that: include: Based on the current visibility level, the obstacle information ahead is obtained by sensing the obstacle situation on the road ahead of the vehicle; According to the current position of the vehicle, obtain the waypoint information of the road section ahead of the vehicle; According to the front obstacle information, the path point information and the collected user response time, a preset fog group response strategy matrix is ​​adjusted to generate a vehicle control strategy; According to the vehicle control strategy, adjust the vehicle's driving path in fog scenes.

2. The vehicle control method for mountain road fog scene according to claim 1 is characterized in that: Based on the current visibility level, the obstacle information ahead is obtained by sensing the obstacle situation on the road ahead of the vehicle, specifically: According to the current visibility level, determine the current fog level, and set the radar sensing priority according to the fog level; Based on the radar sensing priority, obstacles on the road ahead of the vehicle are visually perceived to obtain information about the obstacles ahead.

3. The vehicle control method for mountain road fog scene according to claim 2 is characterized in that: Based on the radar sensing priority, visually perceive the obstacles on the road ahead of the vehicle to obtain the obstacle information ahead, specifically: According to the radar sensing priority, switching to a corresponding sensing device; The distance information and speed information of obstacles on the road section ahead of the vehicle are collected by the sensing device to obtain the obstacle information ahead.

4. The vehicle control method for mountain road fog scene according to claim 1 is characterized in that: The method of obtaining the path point information of the road section ahead of the vehicle according to the current position of the vehicle is specifically as follows: According to the preset navigation map lane path map and the current position of the vehicle, the weather information of the road ahead of the vehicle in the preset future time period and the relevant path points in the fog scene are obtained; The weather information and the relevant path points are integrated to obtain the path point information of the road section ahead of the vehicle.

5. The vehicle control method for mountain road fog scene according to claim 4 is characterized in that: Also includes: The navigation map lane path diagram is updated at a preset frequency.

6. The vehicle control method for mountain road fog scene according to claim 1 is characterized in that: The preset fog coping strategy matrix is ​​adjusted according to the front obstacle information, the path point information and the collected user response time to generate a vehicle control strategy, specifically: The user response time is collected at a preset time interval, and if the user response time does not exceed a first threshold, the front obstacle information and the path point information are processed according to the group fog coping strategy matrix to generate a vehicle control strategy; If the user response time exceeds a first threshold, the response value of the fog group response strategy matrix is ​​adjusted according to the user response time, and the front obstacle information and the path point information are processed by the adjusted fog group response strategy matrix to generate a vehicle control strategy.

7. The vehicle control method for mountain road fog scene according to claim 6 is characterized in that: The step of adjusting the response value of the fog group response strategy matrix according to the user response time is specifically: Optimizing the response value according to the user response time; wherein the response value is related to the vehicle chassis execution parameter; The time point at which the vehicle switches to the user autonomous driving mode can be determined by the vehicle chassis execution parameters.

8. The vehicle control method for mountain road fog scenes according to claim 6 is characterized in that: Also includes: When it is recognized that the vehicle leaves the fog scene, the fog coping strategy matrix is ​​initialized; When it is identified that a vehicle enters a foggy scene, the initialized foggy coping strategy matrix is ​​adjusted according to the user response time.

9. The vehicle control method for mountain road fog scene according to claim 1 is characterized in that: The vehicle control strategy is used to adjust the driving path of the vehicle in the fog scene, specifically: Determine the driving distance between the vehicle and the obstacle on the road ahead according to the vehicle control strategy, and select a first path point from the path point information; The vehicle is driven at the driving distance, and when the vehicle passes the first path point, the intelligent driving system of the vehicle is exited and the vehicle is switched to a user autonomous driving mode.

10. A vehicle control system for mountain road fog scenes, characterized in that: include: Obstacle information collection module, waypoint information collection module, vehicle control strategy generation module and vehicle driving control module; The obstacle information acquisition module is used to obtain the obstacle information ahead by sensing the obstacle situation on the road ahead of the vehicle based on the current visibility level; The waypoint information acquisition module is used to obtain the waypoint information of the road section ahead of the vehicle according to the current position of the vehicle; The vehicle control strategy generation module is used to adjust the preset group fog response strategy matrix according to the front obstacle information, the path point information and the collected user response time, and generate a vehicle control strategy; The vehicle driving control module is used to adjust the vehicle's driving path in fog scenes according to the vehicle control strategy.

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