Vehicle, control method thereof, controller, electronic device, and electronic and electrical system

Abstract

The invention discloses a vehicle and a control method thereof, a controller, an electronic device and an electronic and electrical system.The control method of the vehicle comprises the steps that the scene danger state of the vehicle is determined according to running state information of the vehicle and driving auxiliary information of the vehicle, the driving auxiliary information comprises at least one of the traffic condition and the road parameter of the current road; and controlling the vehicle to run according to a driving strategy corresponding to the scene dangerous state. Therefore, the running state information and the driving auxiliary information in the vehicle driving process are determined, the scene dangerous state of the vehicle is determined according to the running state information and the driving auxiliary information, and the driving strategy is determined according to the scene dangerous state. Dangerous situation early warning analysis and automatic driving transverse control of the vehicle under complex road conditions can be realized, and the driving safety of the vehicle is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle control method, a controller, an electronic device, an electronic and electrical system, and a vehicle. Background Art

[0002] The implementation of autonomous driving technology in related technologies is relatively simple in straight lines, but in complex road conditions and crowded roads, the judgment of the error in the transmission information and the judgment of environmental information by the motor vehicle is not accurate enough, which leads to a relatively complex implementation process of the control strategy. And as the level of autonomous driving increases, the amount of information transmission increases and the types become more diverse. However, related technologies do not have corresponding solutions for how vehicles can identify and process this information and output reasonable control strategies. Summary of the invention

[0003] The first purpose of the present application is to propose a vehicle control method, which determines the operating status information of the vehicle during driving, and determines the driving assistance information during driving, and then determines the scene hazard state of the vehicle according to the operating status information and the driving assistance information, thereby determining the driving strategy according to the scene hazard state.

[0004] The second objective of the present application is to provide a vehicle controller.

[0005] The third objective of the present application is to provide an electronic device.

[0006] The fourth objective of the present application is to provide an electronic and electrical system.

[0007] A fifth object of the present application is to provide a vehicle.

[0008] In order to achieve the above-mentioned objectives, the first aspect embodiment of the present application proposes a vehicle control method, wherein the method includes: determining the scene hazard state of the vehicle based on the vehicle's operating status information and the vehicle's driving assistance information, the driving assistance information including at least one of the current road's traffic conditions and road parameters; controlling the vehicle to drive according to the driving strategy corresponding to the scene hazard state.

[0009] According to the vehicle control method of the embodiment of the present application, by determining the running state information of the vehicle during driving and determining the driving assistance information of the vehicle during driving, the scene danger state of the vehicle is determined according to the running state information and the driving assistance information, and then the driving strategy is determined according to the scene danger state. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0010] In addition, the vehicle control method according to the above embodiment of the present application may also include the following additional technical features:

[0011] According to one embodiment of the present application, the driving strategy includes at least one of a normal driving mode corresponding to a first scenario dangerous state, a protective driving mode corresponding to a second scenario dangerous state, and an emergency driving mode corresponding to a third dangerous state.

[0012] According to one embodiment of the present application, determining the scene dangerous state of the vehicle based on the operating status information of the vehicle and the driving assistance information of the vehicle includes: determining the scene dangerous state judgment coefficient Ω based on the operating status information of the vehicle and the driving assistance information of the vehicle; determining the scene dangerous state of the vehicle based on the scene dangerous state judgment coefficient Ω.

[0013] According to one embodiment of the present application, the operating status information includes one or more of the duration and probability of the scenario, the potential degree of damage of the scenario, the degree of unexpected lateral movement of the vehicle, the yaw angle of the vehicle, the sideslip angle of the center of mass of the vehicle, the sideslip angle limit of the center of mass of the vehicle, the yaw angular velocity limit of the vehicle, and the complexity of the vehicle's operating status; and / or the road parameters include the complexity of the road type, and the driving assistance also includes the complexity of the environmental conditions.

[0014] According to one embodiment of the present application, the scene danger state judgment coefficient Ω satisfies:

[0015]

[0016] Among them, γ0 is the information verification error level coefficient, η1 is the exposure correlation coefficient determined according to the duration of the scenario and the probability of the scenario being sent, η2 is the severity correlation coefficient determined according to the potential degree of injury of the scenario, κ1 is the vehicle hazard correlation coefficient obtained according to the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained according to the complexity of the road type, κ3 is the environmental condition correlation coefficient obtained according to the complexity of the environmental condition, κ4 is the vehicle state correlation coefficient obtained according to the complexity of the vehicle running state, κ5 is the special element correlation coefficient obtained according to the complexity of the special element condition, m is the sideslip angle of the center of mass, m max is the mass center sideslip angle limit value, I is the stability coefficient, e is the yaw angular velocity, w max is the yaw angular velocity limit value.

[0017] According to one embodiment of the present application, if Ω≤80%, the scene dangerous state is the first scene dangerous state; if 80%<Ω≤95%, the scene dangerous state is the second scene dangerous state; if 95%<Ω≤100%, the scene dangerous state is the third dangerous state.

[0018] According to one embodiment of the present application, the information verification error level coefficient γ0 is determined based on the information verification error γ obtained based on the driving assistance information and the operating status information.

[0019] According to one embodiment of the present application, the information verification error γ satisfies:

[0020]

[0021] Among them, ΔS max is the maximum deviation between the actual data and the fitted data, N EO is the full-scale output value, ΔR max is the maximum repetitive difference of forward and reverse strokes, N ET is the duty cycle range, ΔL max is the maximum hysteresis difference within the full range, N TH is the full hysteresis output value, X li is the negative deviation degree of the signal horizontal coordinate value obtained with the same data size, X ri It is the degree of positive deviation of the signal horizontal coordinate value obtained for the same data size.

[0022] According to one embodiment of the present 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, wherein, 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.

[0023] According to one embodiment of the present 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.

[0024] According to one embodiment of the present application, the operating status information is acquired by a control module; the driving assistance information is acquired by a data perception module; and the scene hazard status of the vehicle is determined by an automatic driving module.

[0025] In order to achieve the above-mentioned purpose, the second aspect embodiment of the present application proposes a vehicle controller for executing the steps of the vehicle control method of the aforementioned embodiment of the present application, wherein the controller includes: a data perception module, used to obtain the operating status information and driving assistance information of the vehicle; an automatic driving module, used to determine the scene hazard state according to the operating status information and the driving assistance information, and determine the driving strategy according to the scene hazard state; a control module, used to control the vehicle driving according to the driving strategy.

[0026] According to the controller of the embodiment of the present application, the operating state information of the vehicle during driving is determined by the control module, and the driving assistance information of the vehicle during driving is determined by the data perception module, and then the scene danger state of the vehicle is determined by the automatic driving module according to the operating state information and the driving assistance information, so as to determine the driving strategy according to the scene danger state, and then the control module is used to control the vehicle driving according to the driving strategy. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0027] In addition, the controller according to the above embodiment of the present application may also include the following additional technical features:

[0028] According to one embodiment of the present application, the control module outputs the 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 sensor of the vehicle.

[0029] According to one embodiment of the present application, the data perception module is also used to determine the vehicle's information verification error based on the vehicle operating status information acquired by the control module, and the information verification error is used by the automatic driving module to determine the dangerous state of the scene.

[0030] According to one embodiment of the present application, the controller is a central controller of the vehicle.

[0031] In order to achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes an electronic device, including a processor, the processor is connected to a memory, the memory stores a computer program, and the processor executes the computer program to implement the method of the aforementioned embodiment of the present application.

[0032] According to the electronic device proposed in the embodiment of the present application, by executing a computer program through a processor, dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, thereby improving the safety of vehicle driving.

[0033] In order to achieve the above-mentioned objectives, the fourth aspect embodiment of the present application proposes an electronic and electrical system, wherein the electronic and electrical system includes: the aforementioned electronic device; a sensor, which is communicatively connected to the electronic device, and the sensor is used to obtain the driving assistance information; a first controller, which is communicatively connected to the electronic device, and is used to execute the driving strategy.

[0034] According to the electronic and electrical system of the embodiment of the present application, by determining the operating state information of the vehicle during driving, and determining the driving assistance information of the vehicle during driving through the sensor, the scene dangerous state of the vehicle is determined according to the operating state information and the driving assistance information, thereby determining the driving strategy according to the scene dangerous state, and executing the driving strategy through the first controller. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0035] In addition, the electronic and electrical system according to the above embodiment of the present application may also include the following additional technical features:

[0036] According to one embodiment of the present application, the sensor includes a plurality of visual sensors, distance sensors and / or perception sensors; and the first controller is a driving controller or a lateral controller.

[0037] In order to achieve the above-mentioned objectives, the fifth aspect embodiment of the present application proposes a vehicle, which includes the controller of the aforementioned embodiment of the present application; or includes the electronic device of the aforementioned embodiment of the present application; or includes the electronic and electrical system of the aforementioned embodiment of the present application.

[0038] According to the vehicle of the embodiment of the present application, by adopting the electronic and electrical system of the above embodiment of the present application, it is possible to determine the operating state information of the vehicle during driving, and determine the driving assistance information during driving, and then determine the scene danger state of the vehicle according to the operating state information and the driving assistance information, so as to determine the driving strategy according to the scene danger state. In this way, it is possible to realize the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions, and improve the safety of vehicle driving.

[0039] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of a vehicle control method according to an embodiment of the present application;

[0041] Figure 2 is a flow chart of a vehicle control method according to another embodiment of the present application;

[0042] Figure 3 is a block diagram of an electronic and electrical system according to an embodiment of the present application;

[0043] Figure 4 is a block diagram of a controller according to an embodiment of the present application;

[0044] Figure 5 It is a block diagram of a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0046] The following describes a vehicle control method, a controller, an electronic device, an electronic and electrical system, and a vehicle according to embodiments of the present application with reference to the accompanying drawings.

[0047] The first aspect of the present application provides a vehicle control method, Figure 1 1 is a flow chart of a vehicle control method according to an embodiment of the present application. Specifically, in some embodiments of the present application, the vehicle control method includes:

[0048] S101, determining a scene danger state of a vehicle based on operating state information of the vehicle and driving assistance information of the vehicle, wherein the driving assistance information includes at least one of a current road traffic condition and a road parameter.

[0049] Specifically, in this embodiment, the running state information may include at least one of the following information: vehicle instrument information, vehicle speed information, wheel speed information, and vehicle lateral control state information. The information generated during the vehicle's driving is the running state information, and the running state information can be directly obtained from the vehicle. In addition, the present application may not specifically limit the types of information contained in the running state information.

[0050] The driving assistance information may include at least one of the current road traffic conditions and road parameters, and the driving assistance information may be obtained through visual sensors, distance sensors and / or perception sensors, etc. In addition, the present application may not specifically limit the types of information and sensors contained in the driving assistance information.

[0051] After determining the operating status information and driving assistance information during the vehicle's driving process, the operating status information and driving assistance information are sent to the automatic driving module, and then the automatic driving module can determine the scene hazard status of the vehicle based on the operating status information and driving assistance information.

[0052] S102, controlling the vehicle to travel according to a driving strategy corresponding to a dangerous state of the scene.

[0053] Specifically, in this embodiment, different scene dangerous states correspond to different driving strategies, and then after determining the scene dangerous state according to the running state information and the driving assistance information, the driving strategy can be determined according to the scene dangerous state. For example, when the scene dangerous state is determined to be the first scene dangerous state, it means that the dangerous situation at this time is relatively light, and the electronic and electrical system of the vehicle is sufficient to cope with it, and then the driving strategy can be determined to be the normal driving mode, and the automatic driving function is turned on; when the scene dangerous state is determined to be the second scene dangerous state, it means that the dangerous situation at this time is relatively serious, and then the driving strategy can be determined to be the protection driving mode, and the automatic driving function is turned on and a warning signal is issued; when the scene dangerous state is determined to be the third scene dangerous state, it means that the dangerous situation at this time is serious, and the electronic and electrical system of the vehicle is insufficient to cope with it. At this time, it is necessary to cut off some automatic driving functions and remind the driver, so that the driver can manually take over the vehicle to control the vehicle such as braking. In addition, the present application may not specifically limit the scene dangerous state and the driving strategy corresponding to the scene dangerous state.

[0054] Furthermore, in some embodiments of the present application, the driving strategy includes at least one of a normal driving mode corresponding to a first scenario dangerous state, a protective driving mode corresponding to a second scenario dangerous state, and an emergency driving mode corresponding to a third dangerous state.

[0055] Specifically, in this embodiment, the driving strategy includes a normal driving mode corresponding to the first scenario dangerous state. When the driving strategy is determined to be the normal driving mode, the automatic driving function is turned on, and the control signal is transmitted to the vehicle lateral control module. In the first scenario dangerous state, the system controls the vehicle according to the automatic driving control algorithm. Among them, in the normal driving mode, the automatic driving function may include: intelligent driving function, control mode management function, intelligent navigation function, operation safety capability monitoring function, etc.

[0056] 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 the protective mode, the automatic driving function is turned on 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 automatic driving control algorithm.

[0057] The driving strategy includes an emergency driving mode corresponding to the third dangerous state. The driving strategy is determined to be an emergency mode, some functions of the automatic driving are cut off, and only the emergency functions are retained. The control signal is transmitted to the vehicle's lateral control module and the driver is reminded, giving the driver enough time to take over. In the third dangerous state, the system realizes lateral control of the vehicle according to the driver's manual operation. Among them, in the emergency mode, the emergency functions include: emergency lane keeping assist function, lane departure warning function, emergency avoidance function, etc. In addition, the way to remind the driver can be sound reminder, visual reminder and tactile reminder, etc., and the reminder method is not limited here. Furthermore, through the above driving strategy, the vehicle's dangerous situation warning analysis and automatic driving lateral control under complex road conditions can be realized, improving the safety and comfort of automatic driving.

[0058] Further, in some embodiments of the present application, Figure 2 As shown, determining the scene danger state of the vehicle according to the vehicle's operating state information and the vehicle's driving assistance information includes:

[0059] S201, determining a scene danger state judgment coefficient Ω according to the vehicle's operating state information and the vehicle's driving assistance information.

[0060] The operation state information may include one or more of the duration and probability of the scenario, the potential degree of damage of the scenario, the degree of unexpected lateral movement of the vehicle, the yaw angle of the vehicle, the sideslip angle of the center of mass of the vehicle, the limit value of the sideslip angle of the center of mass of the vehicle, the limit value of the yaw angular velocity of the vehicle, and the complexity of the vehicle operation state. Optionally, the driving assistance information includes at least one of the traffic conditions and road parameters of the current road, and the road parameters may include the complexity of the road type. In addition, the driving assistance may include the complexity of the environmental conditions.

[0061] In some implementations, the scene danger state judgment coefficient Ω satisfies:

[0062]

[0063] Among them, γ0 is the information verification error level coefficient, η1 is the exposure correlation coefficient determined according to the duration of the scene and the probability of the scene being sent, η2 is the severity correlation coefficient determined according to the potential degree of injury of the scene, κ1 is the vehicle hazard correlation coefficient obtained according to the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained according to the complexity of the road type, κ3 is the environmental condition correlation coefficient obtained according to the complexity of the environmental conditions, κ4 is the vehicle state correlation coefficient obtained according to the complexity of the vehicle running state, κ5 is the special element correlation coefficient obtained according to the complexity of the special element conditions, m is the sideslip angle of the center of mass, m max is the mass center sideslip angle limit, I is the stability coefficient, w is the yaw rate, wmax is the yaw rate limit.

[0064] It should be noted that, for the information verification error level coefficient γ0, the value is determined according to the information error verification level, and the value range is 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, and when the information error level is 2, the information verification error level coefficient γ0 is 0.5. Here, no specific limitation is imposed on it. For the exposure correlation coefficient η1, the value is determined according to the duration of the scene and the probability of the scene occurring, and the value range is 0 to 1. The higher the duration of the scene and the probability of the scene occurring, the larger the value. For the severity correlation coefficient η2, the value is determined according to the potential degree of harm caused by the scene to the driver, passengers, people around the vehicle or people in surrounding vehicles, and the value range is 0 to 1. The higher the degree of harm, the larger the value. For the vehicle hazard correlation coefficient κ1, the value is determined according to the degree of unexpected lateral movement of the vehicle, and the value range is 0 to 1. The higher the degree of unexpected lateral movement, the larger the value. For the road type correlation coefficient κ2, the value is determined according to the complexity of the road type. The road types include urban roads, Urban streets, highways, highway on- and off-ramps, roundabouts, rural roads, winding mountain roads, etc., the value range is 0 to 1, and the higher the complexity of the road type, the larger the value; for the environmental condition correlation coefficient κ3, the value is determined according to the complexity of the environmental conditions, including daytime, nighttime, rainy days, heavy fog, extreme weather, etc., the value range is 0 to 1, and the higher the complexity of the environmental conditions, the larger the value; for the vehicle state correlation coefficient κ4, the value is determined according to the complexity of the vehicle operation state, including straight, turning, overtaking, changing lane, emergency avoidance lane change, etc., the value range is 0 to 1. The higher the complexity of vehicle operation, the larger the value; for the special factor association coefficient κ5, the value is determined according to the complexity of the special factor conditions. The special factor conditions include not involved, other vehicles / non-motor vehicles / pedestrians / obstacles on the route, and the distance from the front and rear special factors. The value range is 0 to 1. The higher the complexity of the special factor conditions, the larger the value; for the stability coefficient I, the value is determined according to the actual situation, the value range is 0 to 1, and the lower the stability, the larger the value. In addition, considering the vehicle's maneuverability requirements, the weight of the yaw angular velocity should be greater than the weight of the center of mass sideslip angle. In addition, the application may not specifically limit the correspondence between the information verification error level coefficient γ0 and the information error verification level. The above description is only some implementation methods of the present application, and it is not a limitation on the scope of protection of the present application.

[0065] S202, determining the scene danger state of the vehicle according to the scene danger state judgment coefficient Ω.

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

[0067] The information verification error level coefficient γ0 in the above-mentioned embodiment is determined based on the information verification error γ obtained from the driving assistance information and the running state information. The larger the information verification error γ is, the worse the dangerous state of the vehicle scene is. Therefore, the information verification error level coefficient γ0 is determined by the information verification error γ, and the dangerous state judgment coefficient Ω is calculated based on the information verification error level coefficient γ0, which can improve the stability and safety of vehicle operation.

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

[0069]

[0070] Among them, ΔS max is the maximum deviation between the actual data and the fitted data, N EO is the full-scale output value, ΔR max is the maximum repetitive difference of forward and reverse strokes, N ET is the duty cycle range, ΔL max is the maximum hysteresis difference within the full range, N TH is the full hysteresis output value, X li is the negative deviation degree of the signal horizontal coordinate value obtained with the same data size, X ri It is the degree of positive deviation of the signal horizontal coordinate value obtained for the same data size.

[0071] The information verification error level coefficient can be determined according to the information verification error γ. In some embodiments, 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. In addition, the value range of the information verification error level coefficient γ0 can be set within the range of 0 to 1, that is, the information verification error level coefficient γ0 is greater than or equal to 0 and less than or equal to 1.

[0072] Of course, the information error level in the present application may also adopt other settings. The above descriptions are only some implementation methods of the present application and are not intended to limit the scope of protection of the present application.

[0073] Furthermore, in some embodiments of the present application, the operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the scene hazard status of the vehicle is determined by the automatic driving module.

[0074] Alternatively, if Figure 3 As shown, the data perception module 20 collects driving auxiliary information such as roads, pedestrians, obstacles, etc. during the vehicle's driving process in real time, and receives the operating status information transmitted by the control module 10, and obtains the error verification level through error verification calculation. Then, the data perception module 20 transmits the collected information and the information error verification level to the automatic driving module 30.

[0075] The automatic driving module 30 performs information recognition, identifies specific road information, coordinate information of vehicles, pedestrians and obstacles, vehicle operating status and other information, determines the dangerous state of the scene after comprehensive calculation, outputs different control strategies according to the dangerous state of the scene through the automatic driving algorithm, and transmits it to the control module 10.

[0076] The control module 10 receives information from the automatic driving module 30 and transmits it to the first controller 300 to control the lateral movement of the vehicle, and at the same time receives operating status information transmitted back by the first controller 300 and transmits it to the data perception module 20.

[0077] In summary, according to the vehicle control method of the embodiment of the present application, by determining the operating state information of the vehicle during driving, and determining the driving assistance information of the vehicle during driving, and then determining the scene danger state of the vehicle according to the operating state information and the driving assistance information, the driving strategy is determined according to the scene danger state. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0078] A second aspect of the present application provides a vehicle controller, Figure 4 is a block diagram of a controller according to an embodiment of the present application. Figure 4 As shown, the controller 100 is used to implement the steps of the vehicle control method of the above embodiment of the present application, wherein the controller 100 includes a control module 10, a data perception module 20 and an automatic driving module 30. The controller in the present application can be a regional controller of the vehicle or a central controller of the vehicle.

[0079] Among them, the data perception module 20 is used to obtain the vehicle's operating status information and driving assistance information; the automatic driving module 30 is used to determine the scene's dangerous state based on the operating status information and driving assistance information, and determine the driving strategy based on the scene's dangerous state; the control module 10 is used to control the vehicle's driving according to the driving strategy.

[0080] In some embodiments of the present application, the driving strategy includes at least one of a normal driving mode corresponding to a first scenario dangerous state, a protective driving mode corresponding to a second scenario dangerous state, and an emergency driving mode corresponding to a third dangerous state.

[0081] In some embodiments of the present application, the automatic 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 determine the vehicle's scene danger state based on the scene danger state judgment coefficient Ω.

[0082] In some embodiments of the present application, the operating status information includes one or more of the duration and probability of the scene, the potential degree of damage of the scene, the degree of unexpected lateral movement of the vehicle, the yaw angle of the vehicle, the sideslip angle of the center of mass of the vehicle, the side slip angle limit of the center of mass of the vehicle, the yaw angular velocity limit of the vehicle, and the complexity of the vehicle's operating status; and / or road parameters include the complexity of road types, and driving assistance also includes the complexity of environmental conditions.

[0083] In some embodiments of the present application, the scene danger state judgment coefficient Ω satisfies:

[0084]

[0085] Among them, γ0 is the information verification error level coefficient determined according to the information error level, η1 is the exposure correlation coefficient determined according to the duration of the scene and the probability of the scene being sent, η2 is the severity correlation coefficient determined according to the potential degree of injury of the scene, κ1 is the vehicle hazard correlation coefficient obtained according to the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained according to the complexity of the road type, κ3 is the environmental condition correlation coefficient obtained according to the complexity of the environmental conditions, κ4 is the vehicle state correlation coefficient obtained according to the complexity of the vehicle running state, κ5 is the special element correlation coefficient obtained according to the complexity of the special element conditions, m is the sideslip angle of the center of mass, m max is the mass center sideslip angle limit, I is the stability coefficient, w is the yaw rate, w max is the yaw rate limit.

[0086] In some embodiments of the present application, if Ω≤80%, the scene danger state is the first scene danger state; if 80%<Ω≤95%, the scene danger state is the second scene danger state; if 95%<Ω≤100%, the scene danger state is the third scene danger state.

[0087] In some embodiments of the present application, the information verification error level coefficient γ0 is determined based on the information verification error γ obtained based on the driving assistance information and the operating status information.

[0088] In some embodiments of the present application, the information verification error γ satisfies:

[0089]

[0090] Among them, ΔS max is the maximum deviation between the actual data and the fitted data, N EO is the full-scale output value, ΔR max is the maximum repetitive difference of forward and reverse strokes, N ET is the duty cycle range, ΔL max is the maximum hysteresis difference within the full range, N TH is the full hysteresis output value, X li is the negative deviation degree of the signal horizontal coordinate value obtained with the same data size, X ri It is the degree of positive deviation of the signal horizontal coordinate value obtained for the same data size.

[0091] In some embodiments of the present 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, where 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.

[0092] In some embodiments of the present 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.

[0093] In some embodiments of the present application, the operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the scene hazard status of the vehicle is determined by the automatic driving module.

[0094] It should be noted that other specific implementations of the controller proposed in the embodiment of the present application can refer to the specific implementations of the vehicle control method in the aforementioned embodiment of the present application. In order to reduce redundancy, they will not be repeated here.

[0095] Furthermore, in some embodiments of the present application, the control module outputs the 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.

[0096] Furthermore, in some embodiments of the present application, the data perception module is also used to determine the information verification error of the vehicle based on the vehicle operation status 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 scene.

[0097] Further, in some embodiments of the present application, the controller is a central controller of the vehicle.

[0098] Specifically, in this embodiment, the controller is preferably configured as a central integrated controller to improve data processing capabilities and reduce costs. In addition, the central integrated controller can also be replaced by multiple decentralized controllers, wherein the multiple decentralized controllers include a left domain, a right domain, a front domain, a rear domain, an ADAS (Advanced Driver Assistance Systems) domain, etc. The left domain and the right domain are mainly responsible for controlling the electrical systems on the left and right sides of the vehicle. Specifically, the left controller can control the windows, doors, part of the chassis, part of the power system, etc. on the left side of the vehicle, while the right controller is responsible for the corresponding electrical system on the right side of the vehicle. The front domain and the rear domain 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 sensors at the front and rear. The specific functions and components included vary depending on the car manufacturer and model. The ADAS controller is responsible for processing data from different sensors, such as cameras, millimeter-wave radars and lidars. These sensors provide information about the vehicle's surroundings, and the ADAS controller fuses, analyzes and processes this information to achieve functions such as lane departure warning, adaptive cruise control, and automatic emergency braking.

[0099] In summary, according to the controller of the embodiment of the present application, the operating state information of the vehicle during driving is determined by the control module, and the driving assistance information of the vehicle during driving is determined by the data perception module, and then the scene danger state of the vehicle is determined by the automatic driving module according to the operating state information and the driving assistance information, so as to determine the driving strategy according to the scene danger state, and then the control module is used to control the vehicle driving according to the driving strategy. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0100] Optionally, the first controller is a steering controller or a lateral controller.

[0101] A third aspect of the present application provides an electronic device, which includes a processor, the processor is connected to a memory, the memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0102] A fourth aspect of the present application provides an electronic and electrical system, Figure 3 is a block diagram of an electronic and electrical system according to an embodiment of the present application. Figure 3 As shown, the electronic and electrical system 1000 includes an electronic device, a sensor 200 and a first controller 300 .

[0103] The electronic device may be the electronic device in the aforementioned embodiment, the sensor 200 is connected in communication with the electronic device for acquiring driving assistance information, and the first controller 300 is connected in communication with the electronic device for executing driving strategies.

[0104] Further, in some embodiments of the present application, the sensor includes a plurality of visual sensors, distance sensors and / or perception sensors; and the first controller is a driving controller or a lateral controller.

[0105] In summary, according to the electronic and electrical system of the embodiment of the present application, by determining the operating state information of the vehicle during driving, and determining the driving assistance information of the vehicle during driving through the sensor, the scene dangerous state of the vehicle is determined according to the operating state information and the driving assistance information, thereby determining the driving strategy according to the scene dangerous state, and executing the driving strategy through the first controller. In this way, the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions can be realized, and the safety of vehicle driving can be improved.

[0106] A fifth aspect of the present application provides a vehicle, Figure 5 is a block diagram of a vehicle according to an embodiment of the present application. Figure 5As shown, the vehicle 2000 includes the controller 100 of the above-mentioned embodiment of the present application; or includes the electronic device of the above-mentioned embodiment of the present application; or includes the electronic and electrical system 1000 of the above-mentioned embodiment of the present application.

[0107] According to the vehicle of the embodiment of the present application, by adopting the electronic and electrical system of the above embodiment of the present application, it is possible to determine the operating state information of the vehicle during driving, and determine the driving assistance information during driving, and then determine the scene danger state of the vehicle according to the operating state information and the driving assistance information, so as to determine the driving strategy according to the scene danger state. In this way, it is possible to realize the dangerous situation warning analysis and automatic driving lateral control of the vehicle under complex road conditions, and improve the safety of vehicle driving.

[0108] In addition, other structures and functions of the vehicle of the embodiment of the present application are known to technicians in this field and will not be described here to reduce redundancy.

[0109] 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 specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0110] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0111] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0112] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0113] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0114] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0115] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0116] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A vehicle control method, characterized in that: include: Determining a scene danger state of the vehicle according to the running state information of the vehicle and the driving assistance information of the vehicle, wherein the driving assistance information includes at least one of a traffic condition and a road parameter of a current road; The vehicle is controlled to travel according to a driving strategy corresponding to the dangerous state of the scene.

2. The vehicle control method according to claim 1, characterized in that: The driving strategy includes at least one of a normal driving mode corresponding to a first scenario dangerous state, a protective driving mode corresponding to a second scenario dangerous state, and an emergency driving mode corresponding to a third dangerous state.

3. The vehicle control method according to claim 1 or 2, characterized in that: The determining the scene dangerous state of the vehicle according to the running state information of the vehicle and the driving assistance information of the vehicle comprises: Determining the scene danger state judgment coefficient Ω according to the vehicle operation state information and the vehicle driving assistance information; The scene dangerous state of the vehicle is determined according to the scene dangerous state judgment coefficient Ω.

4. The vehicle control method according to claim 3, characterized in that: The operation status information includes one or more of the duration and probability of the scenario, the potential degree of damage of the scenario, the degree of unexpected lateral movement of the vehicle, the yaw angle of the vehicle, the sideslip angle of the center of mass of the vehicle, the sideslip angle limit of the center of mass of the vehicle, the yaw angular velocity limit of the vehicle, and the complexity of the vehicle operation status; and / or The road parameters include the complexity of road types, and the driving assistance also includes the complexity of environmental conditions.

5. The vehicle control method according to claim 4, characterized in that: The scene danger state judgment coefficient Ω satisfies: Among them, γ0 is the information verification error level coefficient, η1 is the exposure correlation coefficient determined according to the duration of the scenario and the probability of the scenario being sent, η2 is the severity correlation coefficient determined according to the potential degree of injury of the scenario, κ1 is the vehicle hazard correlation coefficient obtained according to the degree of unexpected lateral movement, κ2 is the road type correlation coefficient obtained according to the complexity of the road type, κ3 is the environmental condition correlation coefficient obtained according to the complexity of the environmental condition, κ4 is the vehicle state correlation coefficient obtained according to the complexity of the vehicle running state, κ5 is the special element correlation coefficient obtained according to the complexity of the special element condition, m is the sideslip angle of the center of mass, m max is the mass center sideslip angle limit value, I is the stability coefficient, w is the yaw angular velocity, w max is the yaw angular velocity limit value.

6. The vehicle control method according to claim 5, characterized in that: If Ω≤80%, the scene dangerous state is the first scene dangerous state; if 80%<Ω≤95%, the scene dangerous state is the second scene dangerous state; if 95%<Ω≤100%, the scene dangerous state is the third scene dangerous state.

7. The vehicle control method according to claim 5, characterized in that: 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.

8. The vehicle control method according to claim 7, characterized in that: The information verification error γ satisfies: Among them, ΔS max is the maximum deviation between the actual data and the fitted data, N EO is the full-scale output value, ΔR max is the maximum repetitive difference of forward and reverse strokes, N ET is the duty cycle range, ΔL max is the maximum hysteresis difference within the full range, N TH is the full hysteresis output value, X li is the negative deviation degree of the signal horizontal coordinate value obtained with the same data size, X ri It is the degree of positive deviation of the signal horizontal coordinate value obtained for the same data size.

9. The vehicle control method according to claim 8, characterized in that: The information verification error level coefficient γ0 is positively correlated with the information error verification level, and its value range is 0 to 1, wherein, 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.

10. The vehicle control method according to claim 1, characterized in that: The operating status information includes at least one of vehicle instrument information, vehicle speed information, wheel speed information and vehicle lateral control status information.

11. The vehicle control method according to claim 10, characterized in that: The operating status information is obtained by the control module; the driving assistance information is obtained by the data perception module; and the scene hazard status of the vehicle is determined by the automatic driving module.

12. A vehicle controller, used to implement the steps of the vehicle control method according to any one of claims 1 to 11, characterized in that: The controller comprises: A data sensing module, used to obtain the operating status information and driving assistance information of the vehicle; An automatic driving module, used to determine a scene dangerous state according to the operating state information and the driving assistance information, and determine a driving strategy according to the scene dangerous state; A control module is used to control the vehicle to travel according to the driving strategy.

13. The controller according to claim 12, characterized in that: The control module outputs the 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 sensor of the vehicle.

14. The controller according to claim 12, characterized in that: The data perception module is also used to determine the vehicle's information verification error based on the vehicle's operating status 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 scene.

15. The controller according to claim 12, characterized in that: The controller is a central controller of the vehicle.

16. An electronic device, characterized in that: The method comprises a processor, wherein the processor is connected to a memory, the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 11.

17. An electronic and electrical system, characterized in that: include: The electronic device according to claim 16; A sensor, connected to the electronic device for communication, and used to obtain the driving assistance information; The first controller is connected to the electronic device for executing the driving strategy.

18. The electronic and electrical system according to claim 17, characterized in that: The sensors include a plurality of visual sensors, distance sensors and / or perception sensors; The first controller is a steering controller or a lateral controller.

19. A vehicle, characterized in that: A controller comprising any one of claims 12 to 15; or an electronic device comprising the electronic device according to claim 16; or an electronic and electrical system comprising the electronic and electrical system according to claim 17 or 18.

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