A vehicle control method and system for mountain road group fog scene

Abstract

The application discloses a vehicle control method and system for a mountain road group fog scene, and the method comprises the following steps: obtaining front obstacle information by perceiving the obstacle situation of the road section in front of the vehicle based on the current visibility level; acquiring path point information of the road section in front of the vehicle according to the current position of the vehicle; adjusting a preset group fog response strategy matrix to generate a vehicle control strategy according to the front obstacle information, the path point information and the collected user response time; and adjusting the driving path of the vehicle in the group fog scene according to the vehicle control strategy. The application generates a vehicle control strategy for responding to the group fog scene according to the driving habit of the user and the current driving environment, and improves the driving safety and user experience in foggy weather.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent driving, and particularly relates to a vehicle control method and system for a mountain road group fog scene. BACKGROUND

[0002] Existing intelligent driving systems mainly adopt a strong visual perception scheme, and are equipped with millimeter wave radars to assist in judging front obstacles. However, whether the driver drives independently or the laser radar or strong visual scheme of intelligent driving, in the case of fog scenes, there will be boundary scenes that cannot be coped with.

[0003] When high mountain group fog appears in a mountain road section, the intelligent driving system usually identifies that the camera is blocked, and then quickly enters the mode of user takeover prompt, and the system starts to prepare to exit. If the group fog is relatively large, the system will directly exit and be handed over to the driver for operation. In this extreme case, the time given to the user to cope is less than 5 seconds, which has a great safety risk in the scene of high-speed driving, and the user experience is not good. In order to ensure that the degradation and control in the group fog scene are more smooth, a vehicle control strategy for a mountain road group fog scene is needed to be developed, which can improve the user experience and ensure the driving safety. SUMMARY

[0004] The application provides a vehicle control method and system for a mountain road group fog scene, which generates a vehicle control strategy for coping with a group fog scene according to the driving habits of a user and the current driving environment, and improves the driving safety and user experience in foggy weather.

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

[0006] obtaining front obstacle information by perceiving the obstacle situation of the road section in front of the vehicle based on the current visibility level;

[0007] acquiring path point information of the road section in front of the vehicle according to the current position of the vehicle;

[0008] adjusting a preset group 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;

[0009] adjusting the driving path of the vehicle in the group fog scene according to the vehicle control strategy.

[0010] The above scheme first performs obstacle perception on the road section in front of the vehicle according to the current driving environment to obtain the position information of the obstacle in front of the vehicle, thereby providing data support for avoiding collision in a fog environment due to inability to see the obstacle; then, path point information of the road section in front of the vehicle is collected to provide data support for path planning of the vehicle after entering the fog scene; then, the fast and slow of user reaction is determined through the collected user response time, so as to adjust the fog coping strategy matrix to generate a vehicle control strategy consistent with the driving style of the user, thereby avoiding extreme cases such as switching the vehicle to an autonomous driving mode when the user has not reacted, and improving the driving safety in the fog scene. Finally, based on the vehicle control strategy, a driving path in the fog scene is formulated, which is safer and has a better user experience, so as to ensure safe driving of the vehicle in the fog scene.

[0011] In a possible implementation method of the first aspect, based on the current visibility level, the obstacle situation in front of the vehicle is perceived to obtain the front obstacle information, specifically:

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

[0013] Based on the radar sensing priority, the obstacle in front of the vehicle is visually perceived to obtain the front obstacle information.

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

[0015] In a possible implementation method of the first aspect, based on the radar sensing priority, the obstacle in front of the vehicle is visually perceived to obtain the front obstacle information, specifically:

[0016] According to the radar sensing priority, the corresponding perception device is switched;

[0017] The distance information and speed information of the obstacle in front of the vehicle are collected through the perception device to obtain the front obstacle information.

[0018] In a possible implementation method of the first aspect, the path point information of the road section in front of the vehicle is obtained according to the current position of the vehicle, specifically:

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

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

[0021] The scheme obtains road section information and corresponding weather information in a future period of time according to a navigation map lane path map, and provides data support for subsequent path planning in a foggy scene.

[0022] In a possible implementation method of the first aspect, the navigation map lane path map is updated at a preset frequency.

[0023] In a possible implementation method of the first aspect, a preset foggy scene response strategy matrix is adjusted according to the front obstacle information, the path point information, and the user response time, to generate a vehicle control strategy, specifically as follows.

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

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

[0026] The scheme judges the speed of user reaction according to the user response time. When the user reaction is fast, the foggy scene response strategy matrix does not need to be adjusted; but when the user reaction is slow, the response value of the foggy scene response strategy matrix is adjusted according to the user response time, so that the vehicle control strategy generated by the foggy scene response strategy matrix gives the user enough reaction time to switch to a user autonomous driving mode, and improves the user driving experience.

[0027] In a possible implementation method of the first aspect, the response value of the foggy scene response strategy matrix is adjusted according to the user response time, specifically as follows.

[0028] The response value is optimized according to the user response time; wherein the response value is related to a vehicle chassis execution parameter.

[0029] The vehicle chassis execution parameter can determine a time point at which the vehicle switches to the user autonomous driving mode.

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

[0031] When it is identified that the vehicle leaves the foggy scene, the foggy scene response strategy matrix is initialized.

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

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

[0034] According to the vehicle control strategy, the driving distance between the vehicle and the obstacle in front of the road section is determined, and a first path point is selected from the path point information;

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

[0036] The above scheme determines the driving distance between the vehicle and the obstacle according to the vehicle control strategy, to ensure that the vehicle will not collide with the obstacle in the fog group scene. Meanwhile, the first path point is selected from the path point information, which is used as the timing point for exiting the intelligent driving system of the vehicle and switching the vehicle to the user autonomous driving mode, to provide sufficient reaction time for the user.

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

[0038] The obstacle information acquisition module is configured to obtain the front obstacle information by perceiving the obstacle situation in front of the vehicle based on the current visibility level.

[0039] The path point information acquisition module is configured to obtain the path point information of the road section in front of the vehicle according to the current position of the vehicle.

[0040] The vehicle control strategy generation module is configured to adjust the preset fog group 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.

[0041] The vehicle driving control module is configured to adjust the driving path of the vehicle in the fog group scene according to the vehicle control strategy. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the present application, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0043] Figure 1 is a specific flowchart of a vehicle control method for a mountain road fog group scene provided by an embodiment of the present application;

[0044] Figure 2 This is a system architecture diagram of a vehicle control method for mountain road fog scenarios provided in a certain embodiment of this application;

[0045] Figure 3 This is a typical scenario diagram of a vehicle control method for mountain road fog scenarios provided in a certain embodiment of this application;

[0046] Figure 4 This is a structural diagram of a vehicle control system for mountain road fog scenarios provided in a certain embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0048] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0049] First Embodiment

[0050] When driving on mountain roads, vehicles often encounter dense fog. Current technology addresses this by immediately disengaging the intelligent driving system and alerting the driver that the vehicle is entering autonomous driving mode, where the driver takes control. However, because different drivers have different driving styles, uniformly disengaging the intelligent driving system at the same time interval can prevent slower-reacting or less attentive drivers from adjusting their driving posture in time. This poses a significant safety risk, especially at high speeds, and also results in a poor driving experience for the user.

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

[0052] like Figure 1 As shown, Figure 1 This application provides a detailed flowchart of a vehicle control method for a mountain road fog scene according to a certain embodiment. The vehicle control method for a mountain road fog scene in this embodiment includes steps S1 to S4, which are described in detail below:

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

[0054] In the embodiment of the present application, the vision system of the vehicle is first used to identify the fog level of the current environment, specifically, the visibility is first sensed by the sensor to determine the current fog level, and then the radar sensing priority is set according to the fog level, and the appropriate device is selected to sense the obstacles in the front road section of the vehicle.

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

[0056] The distance information and speed information of the obstacles in the front road section are collected by the millimeter wave radar and other devices to obtain the front obstacle information.

[0057] For example, the obstacles can be roadblock signs and vehicles in front.

[0058] Further, the fog level is identified and switched to the corresponding sensing device based on the trained visual perception model. The visual perception model is formed by sorting and summarizing various pictures of the fog scene defined by the traffic police and collecting the corresponding data on site, and carrying out targeted visual perception algorithm training, so that the model can preliminarily identify the fog.

[0059] Step S2, according to the current position of the vehicle, the path point information of the front road section of the vehicle is obtained.

[0060] In the embodiment of the present application, the navigation map lane path graph is first obtained online from the Internet, and the weather information of the front road section of the vehicle in the future certain time and the related path points in the fog scene are obtained from the navigation map lane path graph according to the current position of the vehicle, so as to plan the path of the vehicle in the future period of time.

[0061] Then the weather information and the related path points are integrated to obtain the path point information of the front road section of the vehicle.

[0062] In order to ensure the real-time and accuracy of the path point information, the navigation map lane path graph of the vehicle is set to be updated regularly.

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

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

[0065] Specifically, when the vehicle is in the fog group scene and starts to exit the intelligent driving system, if the user can respond to the autonomous driving mode within 2 seconds, the user belongs to the fast reaction; otherwise, it means that the user reacts slowly, and a vehicle control strategy for slowly switching the vehicle driving state needs to be generated to provide the user with enough reaction time to adjust his own driving operation.

[0066] The vehicle control strategy in the embodiments of the application is generated based on a small parameter AI model with self-learning ability. The AI model initially has a trained fog group response strategy matrix. If the user's response time does not exceed the set threshold, it means that the user's reaction speed is fast and the matrix does not need to be adjusted, and the front obstacle information and the path point information are directly processed according to the fog group response strategy matrix to generate the vehicle control strategy.

[0067] If the user's 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 overtime, 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 vehicle chassis execution parameters, etc. Through the vehicle chassis execution parameters, the time point and speed at which the vehicle switches to the user autonomous driving mode can be determined to provide more response time for users who are not focused.

[0069] The fog group response strategy matrix mainly determines how many obstacles there are in the road section in the fog group scene, the specific position of the obstacle, the distance between the obstacle and the vehicle, and the turning point of the road section, etc., in combination with the position at which the user's response time is best to exit the intelligent driving system of the vehicle and switch the vehicle to the user autonomous driving mode, so that the user can concentrate on driving the vehicle in the appropriate time and safely pass through the fog road section.

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

[0071] Further, in order to make the fog group response strategy matrix meet the needs of different users, when it is identified that the driver on the driving seat is replaced or the vehicle enters the fog group scene after a long interval, the fog group response strategy matrix will be initialized, and the user's response time will be collected again to adjust the initialized fog group response strategy matrix when the vehicle reenters the fog group scene.

[0072] To better show how the embodiments of the present application generate vehicle control strategies, Figure 2 A decision system architecture diagram is provided. As shown in the figure, the embodiments of the present application are mainly divided into four modules, which are: fusion perception module, map information module, comprehensive decision module and core planning decision module. Among them, the fusion perception module collects the obstacle situation of the road section in front of the vehicle through the perception device, generates the front obstacle information, and sends the front obstacle information 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 in front of the vehicle from the navigation map platform and sends these information to the comprehensive decision module; the comprehensive decision module receives the information provided by the fusion perception module and the map information module, adopts the coping strategy matrix, combines the driver response information, generates the vehicle control strategy conforming to the driver style, and transmits the vehicle control strategy to the core planning decision module, realizing the switching of the vehicle driving mode through the vehicle control strategy.

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

[0074] In the embodiments of the present application, the driving distance between the vehicle and each obstacle in front of the road section is determined according to the vehicle control strategy, and it is ensured that the vehicle and each obstacle can maintain a safe distance before the vehicle and each obstacle in the subsequent driving, so as to avoid collision.

[0075] At the same time, the first path point can also be selected from the path point information according to the vehicle control strategy, and when the vehicle passes through the first path point, the intelligent driving system of the vehicle is exited and the vehicle is switched to the user autonomous driving mode, so as to ensure that the user has enough time to take over the vehicle.

[0076] To better show how the embodiments of the present application provide safe and personalized driving services for drivers through vehicle control strategies, Figure 3 A typical scene diagram is provided to show how the vehicle completes the driving state under the help of the vehicle control strategy in the typical scene.

[0077] As shown in the figure, there are two scenes in the figure, the left is the case that there is no vehicle in front, and the right is the case that there is a vehicle in front. The road section in the cloud cluster is the road section with fog cluster scene, W point is the path point in the fog cluster scene, and vehicle A is the vehicle being driven by the user. When the perception system of vehicle A perceives that there is fog cluster near W point, it is confirmed by the map weather information module that the current road section fog type is heavy fog cluster. At this time, vehicle A uses the navigation map lane path diagram and the related path point information in the fog cluster scene to control the vehicle, and the specific control process is as follows:

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

[0079] (2) When there is a vehicle in front of vehicle A, vehicle A has been identifying vehicle B through the perception device and following vehicle B into the fog group range. At this time, vehicle B is still visible in the millimeter wave radar perception, but it is blurred in the camera image. In combination with this situation, the current fog type is judged and the vehicle control strategy generation mode is entered. Vehicle A will generate a vehicle control strategy according to the path point information and the millimeter wave radar perception information, keep a certain safety distance from vehicle B, refer to the lane stability, and pass through the fog road section safely through the assistance of the path point, and remind the driver to take over at the appropriate time.

[0080] In the above manner, the takeover experience demand of different users in the mountain road high-speed fog scene can be met, and the safety performance of the system is further improved.

[0081] The embodiment of the present application has the following beneficial effects:

[0082] First, the obstacle in front of the vehicle is perceived according to the current driving environment, and the position information of the obstacle in front of the vehicle is obtained, which provides data support for avoiding collision in a fog environment due to inability to see the obstacle; then the path point information of the road section in front of the vehicle is collected, which provides data support for path planning after the vehicle enters the fog scene; then the user response time is collected to determine the speed of user reaction, and the fog group response strategy matrix is adjusted to generate a vehicle control strategy consistent with the driving style of the user, avoiding extreme situations such as switching the vehicle to autonomous driving mode when the user has not reacted, and improving the driving safety in the fog scene. Finally, based on the vehicle control strategy, a safer and more user experience better driving path in the fog scene is developed to ensure safe driving of the vehicle in the fog scene.

[0083] Second embodiment

[0084] Further, in order to execute the vehicle control system for the mountain road fog scene corresponding to the above-mentioned method embodiment, the corresponding functions and technical effects are realized, Figure 4A structural diagram of a vehicle control system for a mountain road group fog scene is provided. For ease of illustration, only parts related to the present embodiment are shown. The vehicle control system for a mountain road group fog scene provided by the present embodiment includes:

[0085] An obstacle information collection module 201 is configured to collect front obstacle information by sensing an obstacle condition of a road segment in front of the vehicle based on a current visibility level.

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

[0087] Then, the radar sensing priority is used to visually sense the obstacle of the road segment in front of the vehicle to obtain the front obstacle information.

[0088] A path point information collection module 202 is configured to collect path point information of the road segment in front of the vehicle according to a current position of the vehicle.

[0089] In the present embodiment, a navigation map lane path map is first collected from the Internet, and weather information of the road segment in front of the vehicle in a future period of time and related path points in a group fog scene are collected from the navigation map lane path map according to the current position of the vehicle, so as to plan a path for the vehicle in the future period of time.

[0090] Then, the weather information and the related path points are integrated to obtain the path point information of the road segment in front of the vehicle.

[0091] To ensure the real-time and accuracy of the path point information, the navigation map lane path map of the vehicle is set to be updated periodically.

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

[0093] In the present embodiment, the user response time is collected at a preset time interval. If the user response time is less than a first threshold value, the front obstacle information and the path point information are processed according to the group fog response strategy matrix to generate the vehicle control strategy.

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

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

[0096] In the embodiments of the present application, the driving distance between the vehicle and each obstacle in the front road section is determined according to the vehicle control strategy, and it is ensured that the vehicle and each obstacle can maintain a safe distance before the vehicle and each obstacle in the subsequent driving, so as to avoid collision.

[0097] Meanwhile, the first path point can be selected from the path point information according to the vehicle control strategy, and when the vehicle passes through the first path point, the intelligent driving system of the vehicle is exited and the vehicle is switched to the user autonomous driving mode, so as to ensure that the user has enough time to take over the vehicle.

[0098] In some embodiments, the obstacle information acquisition module 201 further comprises:

[0099] First, the fog level of the current environment in which the vehicle is located is identified by the vision system of the vehicle, that is, the visibility is sensed by a sensor to determine the current fog level, and then the radar sensing priority is set according to the fog level, and appropriate equipment is selected to sense the obstacles in the front road section of the vehicle.

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

[0101] The distance information and speed information of the obstacles in the front road section are collected by the millimeter wave radar and other equipment to obtain the front obstacle information.

[0102] For example, the obstacles can be roadblock signs and vehicles in front.

[0103] Further, the fog level is identified and switched to the corresponding sensing equipment based on the trained visual sensing model. The visual sensing model is formed by sorting and summarizing various pictures of the foggy scene defined by the traffic police and collecting corresponding data on the spot, and carrying out targeted visual sensing algorithm training, so that the model can preliminarily identify the fog.

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

[0105] When the vehicle is in the foggy scene, the response time of the user is collected through the relevant actions of the user to determine the driving style of the user. Then, the vehicle control strategy closer to the user experience demand is provided according to the driving style of the user.

[0106] Specifically, when the vehicle is in the fog group scene and starts to exit the intelligent driving system, if the user can respond to the autonomous driving mode within 2 seconds, the user belongs to the fast reaction; otherwise, it means that the user reacts slowly, and a vehicle control strategy for slowly switching the vehicle driving state needs to be generated to provide the user with enough reaction time to adjust his driving operation.

[0107] The vehicle control strategy in the embodiments of the present application is generated based on a small parameter AI model with self-learning ability. The AI model initially has a trained fog group response strategy matrix. If the user's response time does not exceed the set threshold, it means that the user's reaction speed is fast and the matrix does not need to be adjusted, and the front obstacle information and the path point information are directly processed according to the fog group response strategy matrix to generate the vehicle control strategy.

[0108] If the user's 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 overtime, 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 vehicle chassis execution parameters, etc. Through the vehicle chassis execution parameters, the time point and speed at which the vehicle switches to the user's autonomous driving mode can be determined to provide more response time for users who are not focused.

[0110] The fog group response strategy matrix mainly determines how many obstacles there are in the road section in the fog group scene, the specific location of the obstacles, the distance between the obstacles and the vehicle, and the turning point of the road section, etc., in combination with the position at which the user's response time is best to exit the vehicle's intelligent driving system and switch the vehicle to the user's autonomous driving mode, so that the user can focus on driving the vehicle autonomously in the appropriate time and safely pass through the fog road section.

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

[0112] Further, in order to make the fog group response strategy matrix meet the needs of different users, when it is identified that the driver on the driving seat is replaced or the vehicle enters the fog group scene after a long interval, the fog group response strategy matrix will be initialized, and the user's response time will be collected again to adjust the initialized fog group response strategy matrix when the vehicle reenters the fog group scene.

[0113] The embodiments of the present application have the following beneficial effects:

[0114] First, according to the current driving environment, the obstacle in front of the vehicle is perceived to obtain the position information of the obstacle in front of the vehicle, so as to provide data support for avoiding collision in a fog environment due to the inability to see the obstacle; then, the path point information of the road section in front of the vehicle is collected to provide data support for path planning after the vehicle enters the fog scene; then, the user response time is collected to determine the speed of user reaction, so as to adjust the fog coping strategy matrix to generate a vehicle control strategy consistent with the driving style of the user, avoid extreme situations such as switching the vehicle to autonomous driving mode when the user has not reacted, and improve the driving safety in the fog scene. Finally, based on the vehicle control strategy, a safer and more user experience better driving path in the fog scene is formulated to ensure that the vehicle can safely drive in the fog scene.

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

Claims

1. A vehicle control method for a mountain road group fog scenario, characterized by, The method comprises the following steps: obtaining front obstacle information by sensing the obstacle situation of the road section in front of the vehicle based on the current visibility level; obtaining path point information of the road section in front of the vehicle according to the current position of the vehicle; adjusting a preset fog response strategy matrix according to the front obstacle information, the path point information and the collected user response time to generate a vehicle control strategy, specifically: collecting the user response time at a preset time interval, if the user response time does not exceed a first threshold, processing the front obstacle information and the path point information according to the fog response strategy matrix to generate a vehicle control strategy; if the user response time exceeds the first threshold, adjusting the response value of the fog response strategy matrix according to the user response time, and processing the front obstacle information and the path point information through the adjusted fog response strategy matrix to generate a vehicle control strategy; adjusting the driving path of the vehicle in the fog scene according to the vehicle control strategy.

2. The hill-approach group-fog scenario-oriented vehicle control method according to claim 1, characterized by, The method comprises the following steps: obtaining front obstacle information by sensing the obstacle situation of the road section in front of the vehicle based on the current visibility level, specifically: judging the current fog level according to the current visibility level, and setting a radar sensing priority according to the fog level; 3. The hill-approach group-fog scenario-oriented vehicle control method according to claim 2, characterized by, based on the radar sensing priority, visually sensing the obstacles in front of the vehicle to obtain the front obstacle information. The method comprises the following steps: based on the radar sensing priority, switching to the corresponding sensing device; 4. The hill-approach group-fog scenario-oriented vehicle control method according to claim 1, characterized by, collecting distance information and speed information of the obstacles in front of the vehicle through the sensing device to obtain the front obstacle information. The method comprises the following steps: obtaining weather information and related path points in the fog scene in front of the vehicle in a preset future time period according to a preset navigation map lane path graph and the current position of the vehicle; 5. The hill-approach group-fog scenario-oriented vehicle control method according to claim 4, characterized by, integrating the weather information and the related path points to obtain the path point information of the road section in front of the vehicle. The method further comprises the following steps:

6. The hill-approach group-fog scenario-oriented vehicle control method according to claim 1, characterized by, updating the navigation map lane path graph at a preset frequency. The method comprises the following steps: optimizing the response value according to the user response time; wherein the response value is related to vehicle chassis execution parameters; 7. The hill-approach group-fog scenario-oriented vehicle control method according to claim 1, characterized by, through the vehicle chassis execution parameters, the time point when the vehicle switches to the user autonomous driving mode can be determined. The method further comprises the following steps: when it is identified that the vehicle leaves the fog scene, initializing the fog response strategy matrix; 8. The hill-approach group-fog scenario-oriented vehicle control method according to claim 1, characterized by, when it is identified that the vehicle enters the fog scene, adjusting the initialized fog response strategy matrix according to the user response time. The method comprises the following steps: determining the driving distance between the vehicle and the obstacles in front of the road section according to the vehicle control strategy, and selecting a first path point from the path point information. The vehicle is driven by 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.

9. A vehicle control system for a mountain road group fog scenario, characterized by, Comprise: Obstacle information acquisition module, path point information acquisition module, vehicle control strategy generation module and vehicle driving control module; Wherein, the obstacle information acquisition module is used for obtaining the front obstacle information by perceiving the obstacle situation of the road section in front of the vehicle based on the current visibility level; The path point information acquisition module is used for obtaining the path point information of the road section in front of the vehicle according to the current position of the vehicle; The vehicle control strategy generation module is used for adjusting the preset fog response strategy matrix according to the front obstacle information, the path point information and the collected user response time, generating a vehicle control strategy, specifically: collecting the user response time at a preset time interval, if the user response time does not exceed the first threshold, processing the front obstacle information and the path point information according to the fog response strategy matrix to generate a vehicle control strategy; if the user response time exceeds the first threshold, adjusting the response value of the fog response strategy matrix according to the user response time, and processing the front obstacle information and the path point information through the adjusted fog response strategy matrix to generate a vehicle control strategy; The vehicle driving control module is used for adjusting the driving path of the vehicle in the fog scene according to the vehicle control strategy.

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