Vehicle and control method and controller therefor, electronic apparatus, and electronic and electrical system

Abstract

A vehicle and a control method and a controller therefor, an electronic apparatus, and an electronic and electrical system. The control method for a vehicle comprises: on the basis of operating state information of a vehicle and travelling assistance information of the vehicle, determining a dangerous scenario state of the vehicle, wherein the travelling assistance information comprises at least one of a traffic condition and a road parameter of the current road; and controlling the vehicle to travel on the basis of a driving strategy corresponding to the dangerous scenario state.

Description

Vehicles and their control methods, controllers, electronic devices and electronic and electrical systems

[0001] Cross-reference to related applications

[0002] This application claims priority to Chinese Patent Application No. 2024106998759, filed on May 30, 2024, entitled "Vehicle and Control Method Thereof, Controller, Electronic Device and Electrical-Electronic System Thereof", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of vehicle control technology, and more particularly to a vehicle control method, controller, electronic device, electronic and electrical system, and vehicle. Background Technology

[0004] While autonomous driving technology is relatively simple to implement on straight paths, it becomes more complex in complex road conditions and congested traffic. This leads to inaccurate judgments of transmitted information errors and environmental information, resulting in a more intricate execution of control strategies. Furthermore, as the level of autonomous driving increases, the quantity and variety of transmitted information also increase. Currently, existing technologies lack solutions for how vehicles can identify and process this information to output appropriate control strategies.

[0005] Application content

[0006] The first objective of this application is to propose a vehicle control method that determines the vehicle's operating status information and driving assistance information during the vehicle's driving process, and then determines the vehicle's scenario danger state based on the operating status information and driving assistance information, thereby determining a driving strategy based on the scenario danger state.

[0007] The second objective of this application is to provide a controller for a vehicle.

[0008] The third objective of this application is to provide an electronic device.

[0009] The fourth objective of this application is to propose an electronic and electrical system.

[0010] The fifth objective of this application is to propose a vehicle.

[0011] To achieve the above objectives, a first aspect of this application proposes a vehicle control method, wherein the method includes: determining a scenario danger state of the vehicle based on the vehicle's operating status information and the vehicle's driving assistance information, wherein the driving assistance information includes at least one of the current road traffic conditions and road parameters; and controlling the vehicle to drive according to a driving strategy corresponding to the scenario danger state.

[0012] The vehicle control method according to embodiments of this application determines the vehicle's operating state information and driving assistance information during driving, and then determines the vehicle's scenario danger state based on the operating state information and driving assistance information, thereby determining a driving strategy based on the scenario danger state. This enables dangerous situation warning analysis and lateral control of the vehicle in complex road conditions, improving vehicle driving safety.

[0013] In addition, the vehicle control method according to the above embodiments of this application may also include the following additional technical features:

[0014] According to one embodiment of this application, the driving strategy includes at least one of a normal driving mode corresponding to a first dangerous scenario, a protective driving mode corresponding to a second dangerous scenario, and an emergency driving mode corresponding to a third dangerous scenario.

[0015] According to one embodiment of this application, determining the scene danger state of the vehicle based on the vehicle's operating status information and the vehicle's driving assistance information includes: determining the scene danger state judgment coefficient Ω based on the vehicle's operating status information and the vehicle's driving assistance information; and determining the scene danger state of the vehicle based on the scene danger state judgment coefficient Ω.

[0016] According to one embodiment of this application, the operating status information includes one or more of the following: scene duration and probability, potential damage level of the scene, degree of unexpected lateral movement of the vehicle, yaw angle of the vehicle, sideslip angle of the vehicle's center of gravity, limit value of the sideslip angle of the vehicle's center of gravity, limit value of the vehicle's yaw rate, and complexity of vehicle operating status; and / or the road parameters include road type complexity, and the driving assistance also includes environmental condition complexity.

[0017] According to one embodiment of this application, the scenario danger state judgment coefficient Ω satisfies:

[0018] Wherein, γ0 is the information verification error level coefficient, η1 is the exposure correlation coefficient determined based on the duration of the scenario and the probability of the scenario being sent, η2 is the severity correlation coefficient determined based on the potential harm level of the scenario, κ1 is the vehicle hazard correlation coefficient obtained based on the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained based on the complexity of the road type, κ3 is the environmental condition correlation coefficient obtained based on the complexity of the environmental conditions, κ4 is the vehicle state correlation coefficient obtained based on the complexity of the vehicle operating state, κ5 is the special element correlation coefficient obtained based on the complexity of special element conditions, and m is the centroid sideslip angle. mac Let I be the limit value of the sideslip angle of the center of mass, I be the stability coefficient, and w be the yaw rate. max This is the limit value of the yaw rate.

[0019] According to one embodiment of this application, if Ω≤80%, the dangerous state of the scene is a first dangerous state; if 80%<Ω≤95%, the dangerous state of the scene is a second dangerous state; and if 95%<Ω≤100%, the dangerous state of the scene is a third dangerous state.

[0020] According to one embodiment of this application, the information verification error level coefficient γ0 is determined based on the information verification error γ obtained from the driving assistance information and the operating status information.

[0021] According to one embodiment of this application, the information verification error γ satisfies:

[0022] Where, ΔS max N represents the maximum deviation between the actual data and the fitted data. EO For the full-scale output value, ΔR mac N represents the maximum repeatability difference between forward and reverse strokes. ET For duty cycle range, ΔL max N represents the maximum hysteresis difference across the entire measurement range. TH X is the full hysteresis output value. li X represents the degree of negative deviation of the signal's abscissa value obtained for the same data size. ri The degree of positive deviation of the signal abscissa value obtained for the same data size.

[0023] According to one embodiment of this application, the information verification error level coefficient γ0 is positively correlated with the information error verification level, and its value range is 0 to 1. Specifically, if 95% < γ ≤ 100%, the information verification error level is 1; if 90% < γ ≤ 95%, the information verification error level is 2; and if γ ≤ 90%, the information verification error level is 3.

[0024] According to one embodiment of this application, the operating status information includes at least one of vehicle instrument information, vehicle speed information, wheel speed information, and vehicle lateral control status information.

[0025] According to one embodiment of this application, the operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the dangerous state of the vehicle in the scene is determined by the autonomous driving module.

[0026] To achieve the above objectives, a second aspect of this application provides a vehicle controller for executing the steps of the vehicle control method described in the preceding embodiments of this application. The controller includes: a data perception module for acquiring vehicle operating status information and driving assistance information; an autonomous driving module for determining a hazardous scene state based on the operating status information and the driving assistance information, and determining a driving strategy based on the hazardous scene state; and a control module for controlling the vehicle's movement according to the driving strategy.

[0027] According to the controller in this application embodiment, the control module determines the vehicle's operating status information during driving, and the data perception module determines the driving assistance information during driving. Then, the autonomous driving module determines the vehicle's hazardous situation based on the operating status information and the driving assistance information, and determines a driving strategy based on the hazardous situation. Finally, the control module controls the vehicle's driving according to the driving strategy. This enables hazardous situation warning analysis and lateral control of the vehicle in complex road conditions, improving driving safety.

[0028] In addition, the controller according to the above embodiments of this application may also include the following additional technical features:

[0029] According to one embodiment of this application, the control module outputs a driving strategy to a first controller so that the vehicle drives according to the driving strategy; and / or the driving assistance information is obtained by the data perception module from the vehicle's sensors.

[0030] According to one embodiment of this application, the data perception module is further configured to determine the vehicle's information verification error based on the vehicle operating status information obtained by the control module, and the information verification error is used by the autonomous driving module to determine the dangerous state of the scene.

[0031] According to one embodiment of this application, the controller is a central controller for a vehicle.

[0032] To achieve the above objectives, a third aspect of this application provides an electronic device including a processor connected to a memory storing a computer program, the processor executing the computer program to implement the methods described in the embodiments of this application.

[0033] The electronic device proposed in the embodiments of this application can realize dangerous situation warning analysis and autonomous driving lateral control of vehicles under complex road conditions by executing computer programs through a processor, thereby improving the safety of vehicle driving.

[0034] To achieve the above objectives, a fourth aspect of this application provides an electronic and electrical system, wherein the electronic and electrical system includes: the aforementioned electronic device; a sensor communicatively connected to the electronic device, the sensor being used to acquire the driving assistance information; and a first controller communicatively connected to the electronic device, used to execute the driving strategy.

[0035] The electronic and electrical system according to embodiments of this application determines the vehicle's operating status information during driving and the driving assistance information during driving through sensors. Based on the operating status information and the driving assistance information, it determines the vehicle's hazardous situation, and then determines a driving strategy based on the hazardous situation. The driving strategy is then executed by a first controller. This enables hazardous situation warning analysis and lateral control of the vehicle in complex road conditions, improving driving safety.

[0036] In addition, the electronic and electrical system according to the above embodiments of this application may also include the following additional technical features:

[0037] According to one embodiment of this application, the sensor includes a plurality of vision sensors, distance sensors and / or perception sensors; the first controller is a driving controller or a lateral controller.

[0038] To achieve the above objectives, a fifth aspect of this application provides a vehicle that includes the controller described in the foregoing embodiments of this application; or the electronic device described in the foregoing embodiments of this application; or the electronic and electrical system described in the foregoing embodiments of this application.

[0039] According to the vehicle embodiments of this application, by employing the electronic and electrical system described above, the vehicle's operating status information and driving assistance information during driving can be determined. Then, based on the operating status information and driving assistance information, the vehicle's hazardous situation can be determined, and a driving strategy can be determined accordingly. This enables hazardous situation warning analysis and lateral control of autonomous driving in complex road conditions, improving vehicle driving safety.

[0040] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0041] Figure 1 is a flowchart illustrating a vehicle control method according to one embodiment of this application;

[0042] Figure 2 is a flowchart illustrating a vehicle control method according to another embodiment of this application;

[0043] Figure 3 is a block diagram of an electronic and electrical system according to an embodiment of this application;

[0044] Figure 4 is a block diagram of the controller according to an embodiment of this application;

[0045] Figure 5 is a block diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0046] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0047] The following description, with reference to the accompanying drawings, describes a vehicle control method, controller, electronic device, electronic and electrical system, and vehicle according to embodiments of this application.

[0048] This application provides a vehicle control method in a first aspect. Figure 1 is a flowchart illustrating the vehicle control method according to one embodiment of this application. Specifically, in some embodiments of this application, the vehicle control method includes:

[0049] S101, determine the vehicle's scenario danger state based on the vehicle's operating status information and the vehicle's driving assistance information, wherein the driving assistance information includes at least one of the current road traffic conditions and road parameters.

[0050] Specifically, in this embodiment, the operating status information may include at least one of the following: vehicle instrument information, vehicle speed information, wheel speed information, and vehicle lateral control status information. Information generated during vehicle operation constitutes operating status information, which can then be directly obtained from the vehicle. Furthermore, this application does not specifically limit the types of information included in the operating status information.

[0051] Driving assistance information may include at least one of the current road traffic conditions and road parameters, and can be acquired through visual sensors, distance sensors and / or perception sensors, etc. Furthermore, this application may not specifically limit the types of information contained in the driving assistance information or the types of sensors.

[0052] After determining the vehicle's operating status information and driving assistance information during the driving process, the operating status information and driving assistance information are sent to the autonomous driving module. The autonomous driving module can then determine the vehicle's dangerous situation based on the operating status information and driving assistance information.

[0053] S102, control the vehicle to drive according to the driving strategy corresponding to the dangerous state of the scene.

[0054] Specifically, in this embodiment, different dangerous scenarios correspond to different driving strategies. After determining the dangerous scenario based on operational status information and driving assistance information, a driving strategy can be determined accordingly. For example, if the dangerous scenario is determined to be a first dangerous scenario, it indicates a relatively minor danger that the vehicle's electronic and electrical systems are sufficient to handle, thus the driving strategy can be determined as normal driving mode, activating the autonomous driving function. If the dangerous scenario is determined to be a second dangerous scenario, it indicates a more serious danger, thus the driving strategy can be determined as protective driving mode, activating the autonomous driving function and issuing a warning signal. If the dangerous scenario is determined to be a third dangerous scenario, it indicates a severe danger that the vehicle's electronic and electrical systems are insufficient to handle, requiring the autonomous driving function to be disconnected and the driver alerted, allowing the driver to manually take over the vehicle for braking and other control actions. Furthermore, this application does not specifically limit the dangerous scenario and the corresponding driving strategy.

[0055] Furthermore, in some embodiments of this application, the driving strategy includes at least one of a normal driving mode corresponding to a first dangerous scenario, a protective driving mode corresponding to a second dangerous scenario, and an emergency driving mode corresponding to a third dangerous scenario.

[0056] Specifically, in this embodiment, the driving strategy includes a normal driving mode corresponding to the first scenario's dangerous state. When the driving strategy is determined to be the normal driving mode, the autonomous driving function is activated, and control signals are sent to the vehicle's lateral control module. Under the first scenario's dangerous state, the system controls the vehicle according to the autonomous driving control algorithm. In the normal driving mode, the autonomous driving function may include: intelligent driving function, control mode management function, intelligent navigation function, and operational safety capability monitoring function, etc.

[0057] The driving strategy includes a protective driving mode corresponding to the dangerous state of the second scenario. When the driving strategy is determined to be in the protective mode, the autonomous driving function is activated and a warning signal is issued. At the same time, the control signal is transmitted to the vehicle's lateral control module. In the dangerous state of the second scenario, the system controls the vehicle according to the autonomous driving control algorithm.

[0058] The driving strategy includes an emergency driving mode corresponding to the third hazardous state. When the driving strategy is set to emergency mode, some autonomous driving functions are disabled, retaining only emergency functions. Control signals are transmitted to the vehicle's lateral control module, and the driver is alerted, given sufficient time to take over. In the third hazardous state, the system achieves lateral control of the vehicle based on the driver's manual operation. Emergency functions in emergency mode include: emergency lane keeping assist, lane departure warning, and emergency avoidance. Furthermore, the driver can be alerted through sound, visual, or tactile means, without limitation on the method. Through this driving strategy, the system can achieve hazardous situation warning analysis and lateral control of the vehicle in complex road conditions, improving the safety and comfort of autonomous driving.

[0059] Furthermore, in some embodiments of this application, as shown in FIG2, determining the vehicle's hazardous situation based on the vehicle's operating status information and driving assistance information includes:

[0060] S201, determine the scenario danger state judgment coefficient Ω based on the vehicle's operating status information and the vehicle's driving assistance information.

[0061] The operational status information may include one or more of the following: scene duration and probability, potential damage level of the scene, degree of unexpected lateral movement of the vehicle, vehicle yaw angle, vehicle sideslip angle, vehicle sideslip angle limit, vehicle yaw rate limit, and complexity of vehicle operational status. Optionally, the driving assistance information may include at least one of the following: current road traffic conditions and road parameters, where road parameters may include road type complexity. Additionally, driving assistance may include environmental condition complexity.

[0062] In some implementations, the scenario danger state judgment coefficient Ω satisfies:

[0063] Wherein, γ0 is the information verification error level coefficient, η1 is the exposure correlation coefficient determined based on the duration of the scene and the probability of scene transmission, η2 is the severity correlation coefficient determined based on the potential harm level of the scene, κ1 is the whole vehicle hazard correlation coefficient obtained based on the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained based on the complexity of road type, κ3 is the environmental condition correlation coefficient obtained based on the complexity of environmental conditions, κ4 is the vehicle state correlation coefficient obtained based on the complexity of vehicle operating state, κ5 is the special element correlation coefficient obtained based on the complexity of special element conditions, and m is the centroid sideslip angle. max Let I be the limit value of the sideslip angle of the center of mass, I be the stability coefficient, and w be the yaw rate. max This represents the limit value of the yaw rate.

[0064] It should be noted that the information verification error level coefficient γ0 is determined based on the information error verification level, ranging from 0 to 1. The higher the information error verification level, the larger the value. For example, when the information error level is 1, the information verification error level coefficient γ0 is 0.2; when the information error level is 2, the information verification error level coefficient γ0 is 0.5. No specific limitation is imposed here. The exposure correlation coefficient η1 is determined based on the duration and probability of the scenario, ranging from 0 to 1. The higher the duration and probability of the scenario, the larger the value. The severity correlation coefficient η2 is determined based on the potential harm to the driver, passengers, people around the vehicle, or people in nearby vehicles, ranging from 0 to 1. The higher the degree of harm, the larger the value. The vehicle hazard correlation coefficient κ1 is determined based on the degree of unexpected lateral vehicle movement, ranging from 0 to 1. The higher the degree of unexpected lateral movement, the larger the value. The road type correlation coefficient κ2 is determined based on the complexity of the road type, which includes urban highways, etc. For city streets, highways, highway ramps, roundabouts, rural roads, and mountain roads, the value ranges from 0 to 1, with higher values ​​indicating greater complexity. For the environmental condition correlation coefficient κ3, the value is determined by the complexity of environmental conditions, including daytime, nighttime, rainy days, heavy fog, and extreme weather, with a range of 0 to 1, and higher values ​​indicating greater complexity. For the vehicle state correlation coefficient κ4, the value is determined by the complexity of vehicle operating states, including straight driving, turning, overtaking, and changing direction. For lane changes and emergency lane evasion, the value ranges from 0 to 1, with larger values ​​for higher complexity of vehicle operation. For the special element correlation coefficient κ5, the value is determined based on the complexity of the special element conditions, which include not involving the vehicle, the presence of other vehicles / non-motorized vehicles / pedestrians / obstacles on the route, and distances to preceding and following special elements. The value ranges from 0 to 1, with larger values ​​for higher complexity of special element conditions. For the stability coefficient I, the value is determined based on the actual situation, ranging from 0 to 1, with larger values ​​for lower stability. Furthermore, considering the vehicle's handling requirements, the weight of the yaw rate should be greater than the weight of the sideslip angle. Additionally, this application does not specifically limit the correspondence between the information verification error level coefficient γ0 and the information error verification level. The above description is merely some implementation methods of this application and is not intended to limit the scope of protection of this application.

[0065] S202, determine the vehicle's scenario danger state based on the scenario danger state judgment coefficient Ω.

[0066] Specifically, in this embodiment, after calculating the scene danger state judgment coefficient Ω, the scene danger state can be determined based on the scene danger state judgment coefficient Ω. For example, when Ω ≤ 80%, the scene danger state is judged as a first scene danger state; when 80% < Ω ≤ 95%, the scene danger state is judged as a second scene danger state; and when 95% < Ω ≤ 100%, the scene danger state is judged as a third scene danger state. Furthermore, this application may not specifically limit the range of the scene danger state judgment coefficient corresponding to the scene danger state. For example, when Ω ≤ 85%, the scene danger state is judged as a first scene danger state. Of course, other settings can also be used for the scene danger state in this application. The above description is merely some implementation methods of this application and is not a limitation on the scope of protection of this application.

[0067] In the aforementioned embodiments, the information verification error level coefficient γ0 is determined based on the information verification error γ obtained from driving assistance information and operating status information. The larger the information verification error γ, the worse the vehicle's hazardous condition. Therefore, determining the information verification error level coefficient γ0 using the information verification error γ, and calculating the hazardous condition judgment coefficient Ω based on the information verification error level coefficient γ0, can improve the stability and safety of vehicle operation.

[0068] Specifically, in some embodiments, the information verification error γ satisfies:

[0069] Where, ΔS max N represents the maximum deviation between the actual data and the fitted data. EO For the full-scale output value, ΔR max N represents the maximum repeatability difference between forward and reverse strokes. ET For duty cycle range, ΔL max N represents the maximum hysteresis difference across the entire measurement range. TH X is the full hysteresis output value. li X represents the degree of negative deviation of the signal's abscissa value obtained for the same data size. ri The degree of positive deviation of the signal abscissa value obtained for the same data size.

[0070] The information verification error level coefficient can be determined based on the information verification error γ. In some implementations, the information verification error γ is divided into multiple information verification error levels, and the information verification error level coefficient is positively correlated with the information verification error level. For example, if 95% < γ ≤ 100%, the information verification error level is 1; if 90% < γ ≤ 95%, the information verification error level is 2; if γ ≤ 90%, the information verification error level is 3. Furthermore, the information verification error level coefficient γ0 can be set to a range from 0 to 1, meaning that the information verification error level coefficient γ0 is greater than or equal to 0 and less than or equal to 1.

[0071] Of course, the information error level in this application can also be set in other ways. The above description is only some implementation methods of this application and is not a limitation on the scope of protection of this application.

[0072] Furthermore, in some embodiments of this application, the operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the dangerous state of the vehicle in the scene is determined by the autonomous driving module.

[0073] Optionally, as shown in Figure 3, the data perception module 20 collects driving assistance information such as roads, pedestrians, and obstacles in real time during the vehicle's driving process, and receives the operating status information transmitted by the control module 10. After error verification calculation, the error verification level is obtained, and then the data perception module 20 transmits the collected information and the information error verification level to the autonomous driving module 30.

[0074] The autonomous driving module 30 performs information recognition, identifying specific road information, coordinate information of vehicles, pedestrians and obstacles, vehicle operating status and other information. After comprehensive calculation, it determines the dangerous state of the scene, and outputs different control strategies according to different dangerous states of the scene through the autonomous driving algorithm, and transmits them to the control module 10.

[0075] The control module 10 receives information from the autonomous driving module 30 and transmits it to the first controller 300 to control the lateral movement of the vehicle. At the same time, it receives the operating status information transmitted back by the first controller 300 and transmits it to the data perception module 20.

[0076] In summary, the vehicle control method according to the embodiments of this application determines the vehicle's operating state information and driving assistance information during the vehicle's driving process, and then determines the vehicle's scenario danger state based on the operating state information and driving assistance information, thereby determining the driving strategy based on the scenario danger state. This enables dangerous situation warning analysis and lateral control of the vehicle in complex road conditions, and improves vehicle driving safety.

[0077] A second aspect of this application provides a vehicle controller, and FIG4 is a block diagram of the controller according to an embodiment of this application. As shown in FIG4, the controller 100 is used to implement the steps of the vehicle control method of the above embodiments of this application, wherein the controller 100 includes a control module 10, a data sensing module 20, and an autonomous driving module 30. The controller in this application can be a vehicle area controller or a vehicle central controller.

[0078] The data perception module 20 is used to acquire vehicle operating status information and driving assistance information; the autonomous driving module 30 is used to determine the dangerous state of the scene based on the operating status information and driving assistance information, and to determine the driving strategy based on the dangerous state of the scene; the control module 10 is used to control the vehicle driving according to the driving strategy.

[0079] In some embodiments of this application, the driving strategy includes at least one of a normal driving mode corresponding to a first dangerous scenario, a protective driving mode corresponding to a second dangerous scenario, and an emergency driving mode corresponding to a third dangerous scenario.

[0080] In some embodiments of this application, the autonomous driving module 30 is specifically used to determine the scene danger state judgment coefficient Ω based on the vehicle's operating status information and the vehicle's driving assistance information; and to determine the scene danger state of the vehicle based on the scene danger state judgment coefficient Ω.

[0081] In some embodiments of this application, the operating status information includes one or more of the following: scene duration and probability, potential damage level of the scene, degree of unexpected lateral movement of the vehicle, yaw angle of the vehicle, sideslip angle of the vehicle's center of gravity, limit value of the sideslip angle of the vehicle's center of gravity, limit value of the vehicle's yaw rate, and complexity of the vehicle's operating status; and / or road parameters include road type complexity, and driving assistance also includes environmental condition complexity.

[0082] In some embodiments of this application, the scenario danger state judgment coefficient Ω satisfies:

[0083] Wherein, γ0 is the information verification error level coefficient determined based on the information error level, η1 is the exposure correlation coefficient determined based on the duration of the scene and the probability of scene transmission, η2 is the severity correlation coefficient determined based on the potential harm level of the scene, κ1 is the whole vehicle hazard correlation coefficient obtained based on the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained based on the complexity of road type, κ3 is the environmental condition correlation coefficient obtained based on the complexity of environmental conditions, κ4 is the vehicle state correlation coefficient obtained based on the complexity of vehicle operating state, κ5 is the special element correlation coefficient obtained based on the complexity of special element conditions, and m is the centroid sideslip angle. max Let I be the limit value of the sideslip angle of the center of mass, I be the stability coefficient, and w be the yaw rate. max This represents the limit value of the yaw rate.

[0084] In some embodiments of this application, if Ω≤80%, the scene danger state is a first scene danger state; if 80%<Ω≤95%, the scene danger state is a second scene danger state; and if 95%<Ω≤100%, the scene danger state is a third scene danger state.

[0085] In some embodiments of this application, the information verification error level coefficient γ0 is determined based on the information verification error γ obtained from driving assistance information and operating status information.

[0086] In some embodiments of this application, the information verification error γ satisfies:

[0087] Where, ΔS max N represents the maximum deviation between the actual data and the fitted data. EO For the full-scale output value, ΔR max N represents the maximum repeatability difference between forward and reverse strokes. ET For duty cycle range, ΔL max N represents the maximum hysteresis difference across the entire measurement range. TH X is the full hysteresis output value. li X represents the degree of negative deviation of the signal's abscissa value obtained for the same data size. ri The degree of positive deviation of the signal abscissa value obtained for the same data size.

[0088] In some embodiments of this application, the information verification error level coefficient γ0 is positively correlated with the information error verification level, and the value range is 0 to 1. Specifically, if 95% < γ ≤ 100%, the information verification error level is 1; if 90% < γ ≤ 95%, the information verification error level is 2; and if γ ≤ 90%, the information verification error level is 3.

[0089] In some embodiments of this application, the operating status information includes at least one of vehicle instrument information, vehicle speed information, wheel speed information, and vehicle lateral control status information.

[0090] In some embodiments of this application, the operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the dangerous state of the vehicle in the scene is determined by the autonomous driving module.

[0091] It should be noted that other specific implementations of the controller proposed in this application can be found in the specific implementations of the vehicle control method described in the foregoing embodiments of this application. To reduce redundancy, they will not be repeated here.

[0092] Furthermore, in some embodiments of this application, the control module outputs a driving strategy to the first controller so that the vehicle drives according to the driving strategy; and / or, the driving assistance information is obtained by the data perception module from the vehicle's sensors.

[0093] Furthermore, in some embodiments of this application, the data perception module is also used to determine the vehicle's information verification error based on the vehicle operating status information obtained by the control module. The information verification error is used by the autonomous driving module to determine the dangerous state of the scene.

[0094] Furthermore, in some embodiments of this application, the controller is the central controller of the vehicle.

[0095] Specifically, in this embodiment, the controller is preferably configured as a centrally integrated controller to improve data processing capabilities and reduce costs. Alternatively, multiple distributed controllers can replace the centrally integrated controller. These distributed controllers include left, right, front, rear, and ADAS (Advanced Driver Assistance Systems) domains. The left and right domains primarily control the electrical systems on the left and right sides of the vehicle. Specifically, the left controller can control the left-side windows, doors, part of the chassis, and part of the powertrain, while the right controller is responsible for the corresponding electrical systems on the right side. The front and rear domains mainly refer to the control functions related to the front and rear of the vehicle body, covering the control of components such as headlights, taillights, wipers, and front and rear sensors. The specific functions and included components vary depending on the car manufacturer and model. The ADAS controller processes data from various sensors, such as cameras, millimeter-wave radar, and lidar. These sensors provide information about the vehicle's surrounding environment, which the ADAS controller then fuses, analyzes, and processes to achieve functions such as lane departure warning, adaptive cruise control, and automatic emergency braking.

[0096] In summary, the controller according to the embodiments of this application determines the vehicle's operating status information during driving through a control module and the driving assistance information during driving through a data perception module. Then, the autonomous driving module determines the vehicle's hazardous situation based on the operating status information and the driving assistance information, and determines a driving strategy based on the hazardous situation. Finally, the control module controls the vehicle's driving according to the driving strategy. Therefore, it can realize hazardous situation warning analysis and autonomous driving lateral control of the vehicle in complex road conditions, and improves the safety of vehicle driving.

[0097] Optionally, the first controller is a driving controller or a lateral controller.

[0098] A third aspect of this application provides an electronic device including a processor connected to a memory storing a computer program, the processor executing the computer program to implement the aforementioned method.

[0099] A fourth aspect of this application provides an electronic and electrical system, and FIG3 is a block diagram of the electronic and electrical system according to an embodiment of the present application. As shown in FIG3, the electronic and electrical system 1000 includes an electronic device, a sensor 200, and a first controller 300.

[0100] The electronic device can be the same as the one described in the previous embodiment. The sensor 200 is communicatively connected to the electronic device and is used to acquire driving assistance information. The first controller 300 is communicatively connected to the electronic device and is used to execute driving strategies.

[0101] Furthermore, in some embodiments of this application, the sensor includes multiple vision sensors, distance sensors, and / or perception sensors; the first controller is a driving controller or a lateral controller.

[0102] In summary, the electronic and electrical system according to the embodiments of this application determines the vehicle's operating status information during driving and the driving assistance information during driving through sensors. Based on the operating status information and the driving assistance information, it determines the vehicle's hazardous situation, and then determines a driving strategy based on the hazardous situation, which is executed by a first controller. This enables hazardous situation warning analysis and lateral control of the vehicle in complex road conditions, improving driving safety.

[0103] A fifth aspect of this application provides a vehicle, and FIG5 is a block diagram of a vehicle according to an embodiment of this application. As shown in FIG5, the vehicle 2000 includes the controller 100 of the above-described embodiment of this application; or includes the electronic device of the above-described embodiment of this application; or includes the electronic and electrical system 1000 of the above-described embodiment of this application.

[0104] According to the vehicle embodiments of this application, by employing the electronic and electrical system described above, the vehicle's operating status information and driving assistance information during driving can be determined. Then, based on the operating status information and driving assistance information, the vehicle's hazardous situation can be determined, and a driving strategy can be determined accordingly. This enables hazardous situation warning analysis and lateral control of autonomous driving in complex road conditions, improving vehicle driving safety.

[0105] Furthermore, other components and functions of the vehicle in the embodiments of this application are known to those skilled in the art, and will not be described in detail here to reduce redundancy.

[0106] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0107] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0108] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0109] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0111] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0112] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0113] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A control method of a vehicle, wherein, The method comprises: determining a scene danger state of the vehicle according to operating state information of the vehicle and driving assistance information of the vehicle, the driving assistance information comprising at least one of traffic conditions and road parameters of a current road; controlling the vehicle to travel according to a driving strategy corresponding to the scene danger state.

2. The control method of a vehicle according to claim 1, wherein The driving strategy comprises at least one of a normal driving mode corresponding to a first scene danger state, a protection driving mode corresponding to a second scene danger state, and an emergency driving mode corresponding to a third danger state.

3. The control method of a vehicle according to claim 1 or 2, wherein The determination of the scene danger state according to the operating state information of the vehicle and the driving assistance information of the vehicle comprises: determining a scene danger state judgment coefficient Ω according to the operating state information of the vehicle and the driving assistance information of the vehicle; determining the scene danger state of the vehicle according to the scene danger state judgment coefficient Ω.

4. The control method of a vehicle according to claim 3, wherein The operating state information comprises one or more of a scene duration and probability, a potential damage degree of the scene, an unexpected lateral motion degree of the vehicle, a yaw angle of the vehicle, a center of mass side slip angle of the vehicle, a center of mass side slip angle limit value of the vehicle, a yaw angular velocity limit value of the vehicle, and a vehicle operating state complexity degree; and / or The road parameters comprise a road type complexity degree, and the driving assistance further comprises an environmental condition complexity degree.

5. The control method of a vehicle according to claim 4, wherein The scene danger state judgment coefficient Ω satisfies: wherein γ0 is a coefficient of information check error level, η1 is a coefficient of exposure relevance determined according to the duration of the scene and the probability of transmission of the scene, η2 is a coefficient of severity relevance determined according to the potential harm degree of the scene, κ1 is a coefficient of vehicle harm relevance obtained according to the degree of the unintended lateral movement, κ2 is a coefficient of road type relevance obtained according to the complexity degree of the road type, κ3 is a coefficient of environmental condition relevance obtained according to the complexity degree of the environmental condition, κ4 is a coefficient of vehicle state relevance obtained according to the complexity degree of the vehicle operating state, κ5 is a coefficient of special element relevance obtained according to the complexity degree of the special element condition, m is the centroid side slip angle, m max is the centroid side slip angle limit value, I is a coefficient of stability, w is the yaw rate, w max is the yaw rate limit value.

6. The control method of a vehicle according to claim 5, wherein If Ω≤80%, the scene danger state is a first scene danger state; If 80%<Ω≤95%, the scene danger state is a second scene danger state; If 95%<Ω≤100%, the scene danger state is a third scene danger state.

7. The control method of a vehicle according to claim 5, wherein The information verification error level coefficient γ0 is determined according to an information verification error γ obtained from the driving assistance information and the operating state information.

8. The control method of a vehicle according to claim 7, wherein The information check error γ satisfies: Wherein, ΔS max is the maximum deviation value of actual data and fitting data, N EO is the full-scale output value, ΔR max is the maximum repeated difference value of positive and negative strokes, N ET is the duty cycle range, ΔL max is the maximum hysteresis difference in the full range, N TH is the full hysteresis output value, X li is the negative deviation degree of signal abscissa value obtained by the same data size, X ri is the positive deviation degree of signal abscissa value obtained by the same data size.

9. The control method of a vehicle according to claim 8, wherein The information verification error level coefficient γ0 is positively correlated with an information error verification level, and the value range is 0-1, wherein if 95%<γ≤100%, the information verification error level is 1; if 90%<γ≤95%, the information verification error level is 2; and if γ≤90%, the information verification error level is 3.

10. The control method of a vehicle according to any one of claims 1-9, wherein, The operating state information comprises at least one of automobile instrument information, vehicle speed information, wheel speed information, and vehicle lateral control state information.

11. The control method of a vehicle according to claim 10, wherein The operating state information is obtained by a control module, the driving assistance information is obtained by a data perception module, and the scene danger state of the vehicle is determined by an automatic driving module.

12. A controller of a vehicle for implementing the steps of the control method of the vehicle according to any one of claims 1 to 11, wherein, The controller comprises: a data perception module configured to obtain operating state information and driving assistance information of the vehicle; an automatic driving module configured to determine a scene danger state according to the operating state information and the driving assistance information, and determine a driving strategy according to the scene danger state; a control module configured to control the vehicle to travel according to the driving strategy.

13. The controller of claim 12, wherein, The control module outputs the driving strategy to a first controller, so that the vehicle travels according to the driving strategy; and / or the driving assistance information is obtained by the data perception module from sensors of the vehicle.

14. The controller of claim 12 or 13, wherein, The data perception module is further configured to determine an information verification error of the vehicle according to vehicle running state information obtained by the control module, and the information verification error is used by the automatic driving module to determine the dangerous state of the scenario.

15. The controller of any one of claims 12-14, wherein, The controller is a central controller of the vehicle.

16. An electronic device, wherein, A processor is connected with a memory, and the memory stores a computer program, and the processor executes the computer program to implement the method in any one of claims 1-11.

17. An electronic and electric system, wherein The method comprises: The electronic device of claim 16; A sensor is connected with the electronic device, and the sensor is configured to obtain the driving assistance information. A first controller is connected with the electronic device, and the first controller is configured to execute the driving strategy.

18. The electronic and electric system according to claim 17, wherein The sensor comprises a plurality of visual sensors, distance sensors and / or perception sensors. The first controller is a driving controller or a lateral controller.

19. A vehicle, wherein, The method comprises the controller of any one of claims 12-15; or the electronic device of claim 16; or the electronic system of claim 17 or 18.

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