Vehicle control method and device, vehicle and storage medium

Abstract

The invention relates to the field of intelligent driving, in particular to a vehicle control method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring vehicle related information, wherein the vehicle related information comprises at least one of vehicle motion information, road surface state information and contact state information of tires and the ground; determining safety state grade information of the vehicle according to the vehicle related information; the safety state level information is used for representing that the vehicle is in a safety state or unstable states with different severity degrees; and controlling a chassis system of the vehicle according to the safety state level information. Therefore, the safety state grade information is determined by using multi-dimensional data such as the vehicle motion information, the road surface state information and the tire and ground contact state information, the safety state grade information of the vehicle can be comprehensively and accurately evaluated, the suspension of the vehicle is further controlled, the vehicle instability probability is effectively reduced, and the safety of the vehicle is improved. And dangerous conditions such as rollover and drifting are reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent driving, and in particular to a vehicle control method, device, vehicle, and storage medium. Background Art

[0002] With the continuous advancement of automotive technology, people are placing higher demands on vehicle comfort. However, traditional suspension systems suffer from significant response delays during adjustment, making it difficult to quickly adapt to complex and changing road conditions. This delay not only affects vehicle comfort but can also compromise vehicle handling and safety in emergency situations. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a vehicle control method, device, vehicle and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, there is provided a vehicle control method, comprising: Acquiring vehicle-related information, the vehicle-related information including at least one of vehicle motion information, road surface state information, and tire-ground contact state information; Determining safety status level information of the vehicle based on the vehicle-related information; the safety status level information is used to indicate whether the vehicle is in a safe state or an unstable state of varying severity; The chassis system of the vehicle is controlled according to the safety status level information.

[0005] In this technical solution, multi-dimensional data, including vehicle motion information, road surface conditions, and tire-ground contact status, is used to determine the vehicle's safety status, enabling a comprehensive and accurate assessment of the vehicle's safety status. This safety status information characterizes whether the vehicle is in a safe state or unstable states of varying severity. Controlling the vehicle's chassis system based on this safety status information effectively reduces the probability of vehicle instability, minimizing the risk of rollovers, skidding, and other dangerous situations, ensuring the safety of both occupants and the vehicle, and providing a smoother and more comfortable ride.

[0006] In an optional implementation manner, determining the safety status level information of the vehicle based on the vehicle-related information includes: Performing time alignment processing on the vehicle-related information to obtain multi-source data spatiotemporal information; The security status level information is determined based on the spatiotemporal information of the multi-source data.

[0007] In the above technical solution, time alignment of vehicle-related information can ensure their temporal consistency, thereby improving the accuracy of the determined safety status level information.

[0008] In an optional implementation, determining the security status level information based on the spatiotemporal information of the multi-source data includes: The security status level information is determined by using a security status level information determination model and based on the spatiotemporal information of the multi-source data.

[0009] In the above technical solution, the use of a pre-trained model to determine the safety status level information can complete complex calculation tasks in a short time, more accurately determine the safety status level information, reduce the probability of misjudgment, and ensure vehicle safety.

[0010] In an optional embodiment, the safety status level information determination model is further used to determine the friction coefficient of the road surface based on the spatiotemporal information of the multi-source data.

[0011] In the above technical solution, the accuracy of the determined friction coefficient can be improved by using the safety status level information to determine the model.

[0012] In an optional embodiment, the security status level information determination model is trained in the following manner: Acquire multiple pieces of training sample data, each piece of the training sample data including a multi-source data spatiotemporal information sample and a security status level information sample corresponding to the multi-source data spatiotemporal information sample; The neural network model is trained using the training sample data until a training end condition is met, thereby obtaining the safety status level information determination model.

[0013] In the above technical solution, the model can learn internal rules and patterns from training sample data, improve generalization ability, and utilize new multi-source data spatiotemporal information to accurately determine the vehicle's safety status level information.

[0014] In an optional implementation, before performing time alignment processing on the vehicle-related information, filtering processing is performed on the vehicle-related information, so as to perform time alignment processing on the filtered vehicle-related information.

[0015] In the above technical solution, data quality and availability can be improved, providing a more reliable basis for subsequent determination of security status level information.

[0016] In an optional embodiment, the vehicle motion information includes at least one of vehicle speed, steering information, and vehicle body posture information; the road surface state information includes at least one of road surface texture information and obstacle information; and the contact state information includes at least one of friction coefficient and ground pressure.

[0017] In the above technical solution, the comprehensiveness of vehicle-related information can be guaranteed.

[0018] In an optional embodiment, the chassis system includes a suspension; and controlling the chassis system of the vehicle according to the safety status level information includes: Determining a target level for suspension stiffness adjustment based on the safety status level information; wherein the target level is used to represent the adjustment level of the suspension stiffness; Based on the target level, the stiffness of the suspension is adjusted.

[0019] In the above technical solution, precise adjustment of the suspension stiffness can be achieved by determining the target level of the suspension stiffness adjustment.

[0020] In an optional embodiment, determining a target level of suspension stiffness adjustment according to the safety status level information includes: A target level of suspension stiffness adjustment is determined based on the safety status level information and reference information, wherein the reference information includes at least one of a road scene and vehicle body posture information in the vehicle motion information.

[0021] In the above technical solution, based on the safety status level information, combined with at least one of the road scene and vehicle body posture information, the possibility of the vehicle losing stability can be determined more accurately, further improving the accuracy of the determined target level and achieving precise control of the vehicle.

[0022] In an optional embodiment, The determining a target level of suspension stiffness adjustment according to the safety status level information and reference information includes: If the safety status level information indicates that the vehicle is in a safe state, the road scene is a dry road, and the change value of the vehicle body posture information within a first preset time period is less than a first threshold, then determining that the target level is the first level; If the safety status level information indicates that the vehicle is in a safe state, the road scene is a slippery road, and the change value of the vehicle body posture information within the second preset time period is less than a second threshold, then determining that the target level is the second level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is a slippery road, and the change value of the vehicle body posture information within a third preset time period is less than a third threshold, then determining that the target level is the third level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is icy or snowy, and the change in the vehicle posture information within a fourth preset time period is less than a fourth threshold, then determining that the target level is the fourth level; If the safety status level information indicates that the vehicle has become unstable, the road scene is an icy or snowy road, and the change value of the vehicle body posture information within a fifth preset time period is greater than a fifth threshold, then the target level is determined to be the fifth level; If the safety status level information indicates that the vehicle has become unstable, and the vehicle body posture information continues to fluctuate within a sixth preset time period, and the fluctuation amplitude is greater than a sixth threshold, then determining that the target level is the sixth level; The first level is lower than the second level, the second level is lower than the third level, the third level is lower than the fourth level, the fourth level is lower than the fifth level, and the fifth level is lower than the sixth level.

[0023] In the above technical solution, the safety status level information, road scene and vehicle body posture information fluctuation amplitude can be specifically combined to achieve precise control of the vehicle.

[0024] In an optional embodiment, the method further includes at least one of the following: When a preset condition is met, displaying the safety status level information and / or the target level using a display device in the vehicle; When the preset conditions are met, using a speaker in the vehicle to voice announce the safety status level information and / or the target level; When the preset conditions are met, the vibration device in the vehicle is used to feed back the safety status level information and / or the target level through vibration amplitude.

[0025] In the above technical solution, users can understand the vehicle's operating status in a timely manner, which helps drivers to detect potential dangers in a timely manner and take measures, thereby improving the riding experience.

[0026] In an optional embodiment, the preset condition includes at least one of the following: The safety status level information is greater than a preset stability threshold; The target level is greater than a preset level threshold; A difference between the target level and the adjusted front suspension stiffness level is greater than a difference threshold.

[0027] In the above technical solution, the user can be prompted when there is a high possibility that the vehicle will lose stability or when it is determined that the vehicle suspension stiffness will be significantly adjusted, so that the user can be prepared and avoid a decline in user experience due to sudden adjustments.

[0028] According to a second aspect of an embodiment of the present disclosure, a vehicle control device is provided, which is used to implement the steps of the vehicle control method provided in the first aspect of the present disclosure.

[0029] According to a third aspect of an embodiment of the present disclosure, there is provided a vehicle, comprising: processor; a memory for storing processor-executable instructions; The processor is configured to execute the executable instructions in the memory to implement the steps of the vehicle control method provided in the first aspect of the present disclosure.

[0030] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the vehicle control method provided in the first aspect of the present disclosure are implemented.

[0031] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the vehicle control method provided in the first aspect of the present disclosure.

[0032] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0034] Figure 1 The figure is a flow chart showing a vehicle control method according to an exemplary embodiment.

[0035] Figure 2 The figure is a flow chart showing a vehicle control method according to an exemplary embodiment.

[0036] Figure 3 The figure is a flow chart showing a vehicle control method according to an exemplary embodiment.

[0037] Figure 4 is a block diagram of a vehicle control device according to an exemplary embodiment.

[0038] Figure 5 is a block diagram of a vehicle control device according to an exemplary embodiment.

[0039] Figure 6 is a block diagram of a vehicle control device according to an exemplary embodiment.

[0040] Figure 7 is a block diagram of a vehicle according to an exemplary embodiment.

[0041] Figure 8 is a block diagram of a chip system according to an exemplary embodiment. DETAILED DESCRIPTION

[0042] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0043] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the corresponding data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0044] Figure 1 FIG. 1 is a flow chart of a vehicle control method according to an exemplary embodiment. The method can be applied to a vehicle, for example, to a controller of a vehicle. Figure 1 As shown, the method may include the following steps.

[0045] In step S101 , vehicle-related information is obtained.

[0046] The vehicle-related information includes at least one of vehicle motion information, road surface state information, and tire-ground contact state information.

[0047] In one embodiment, vehicle motion information includes at least one of vehicle speed, steering information, and vehicle body posture information. Vehicle speed may be obtained using a speed sensor pre-installed on the vehicle. Steering information may include the vehicle's steering angle, which may be obtained using a steering angle sensor pre-installed on the vehicle and / or an inertial measurement unit. Vehicle body posture information may include yaw angle and roll angle. Vehicle body posture information may be obtained by calculating the vehicle's acceleration and angular velocity using an inertial measurement unit pre-installed on the vehicle.

[0048] In one embodiment, road surface condition information includes at least one of road surface texture information and obstacle information. A vehicle may be equipped with a laser radar and a camera, which can be used to identify road surface texture information and obstacle information through fusion of the laser radar and camera. For example, the captured road surface image can be pre-processed using grayscale conversion, filtering, edge detection, and other methods, and texture information can be extracted from the pre-processed image.

[0049] In one embodiment, the contact state information includes at least one of a friction coefficient and a ground contact pressure. The friction coefficient can be determined using data collected by a multi-axis acceleration sensor pre-installed inside the tire. The multi-axis acceleration sensor can collect more information, and therefore, the friction coefficient can be determined with higher accuracy using the multi-axis acceleration sensor. The data collected by the multi-axis acceleration sensor can be used to determine the actual friction force. , and the friction coefficient The actual friction force collected can be used and the preset friction standard value Determine, for example, the coefficient of friction = The ground pressure can be obtained by using a pressure sensor pre-installed on the vehicle.

[0050] Obtaining vehicle-related information can provide a data basis for the subsequent evaluation of safety status level information.

[0051] In step S102, the safety status level information of the vehicle is determined based on the vehicle-related information.

[0052] The safety status level information is used to indicate whether the vehicle is in a safe state or an unstable state of varying severity.

[0053] In one embodiment, a large amount of vehicle driving data and instability cases can be used to construct training samples, and then a safety status level information determination model can be obtained through training. During the application of the safety status level information determination model, vehicle-related information can be used as input and safety status level information can be used as output.

[0054] The instability state can represent the possibility of the vehicle losing stability due to various internal and external factors during driving. The safety status level information can be quantified using a numerical value. The larger the numerical value, the greater the instability risk represented by the safety status level information. For example, if the safety status level information is 0, it can be characterized as the vehicle being in a safe state and there is no obvious instability risk; if the safety status level information is 1, it can be characterized as the risk of instability of the vehicle, but it will not pose a substantial threat to the driving safety of the vehicle; if the safety status level information is 2, it can be characterized as the vehicle being unstable and the vehicle is in an extremely dangerous state. In actual application, the safety status level information can be quantified in a more detailed manner, for example, using a numerical range of 0-100, and defining the states corresponding to different numerical intervals in detail, which will not be elaborated here.

[0055] In this way, by comprehensively acquiring multi-dimensional data such as vehicle movement information, road condition information, and tire-ground contact status information to determine the safety status level information, the vehicle's current status and potential dangers can be perceived more comprehensively and accurately.

[0056] In step S103 , the chassis system of the vehicle is controlled according to the safety status level information.

[0057] In one embodiment, the chassis system may include a suspension and / or tire pressure. The following description will take the control of the vehicle's suspension as an example.

[0058] In one embodiment, when the safety status level information indicates that the vehicle is in a safe state, the suspension system may maintain a default operating mode to provide a good balance between comfort and handling. For example, during normal driving on a flat road, the suspension stiffness is moderate and the vehicle body height is at a standard position.

[0059] In one embodiment, when the safety status level information indicates a risk of vehicle instability, the suspension system can automatically adjust the suspension stiffness and vehicle height. For example, when encountering slightly bumpy roads or cornering, the suspension stiffness can be increased, while the vehicle height can be adjusted based on road conditions to maintain vehicle stability.

[0060] In one embodiment, when the safety status level information indicates that the vehicle has become unstable, the suspension system can control the stiffness of the suspension to increase significantly to provide stronger support and improve vehicle stability; at the same time, the vehicle body height can be controlled to decrease, thereby further improving stability by lowering the center of gravity of the vehicle.

[0061] In this way, through real-time adjustment of the suspension, the vehicle can always maintain a stable driving posture, enhancing the comfort of the occupants and driving safety.

[0062] In this technical solution, multi-dimensional data, including vehicle motion information, road surface conditions, and tire-ground contact status, is used to determine the vehicle's safety status, enabling a comprehensive and accurate assessment of the vehicle's safety status. This safety status information characterizes whether the vehicle is in a safe state or unstable states of varying severity. Controlling the vehicle's chassis system based on this safety status information effectively reduces the probability of vehicle instability, minimizing the risk of rollovers, skidding, and other dangerous situations, ensuring the safety of both occupants and the vehicle, and providing a smoother and more comfortable ride.

[0063] In some possible implementations, in step S102, determining the vehicle's safety status level information based on the vehicle-related information includes: Perform time alignment processing on vehicle-related information to obtain spatiotemporal information of multi-source data; Determine security status level information based on spatiotemporal information of multi-source data.

[0064] For example, a timestamp can be added to the data of each sensor to record the exact time of data collection, and the timestamp can be used to time-align the vehicle-related information. For another example, an interpolation method can be used to time-align the vehicle-related information.

[0065] Since different sensors may have different sampling frequencies, data transmission delays, and processing times, time alignment of these vehicle-related information before fusion can ensure their temporal consistency, thereby improving the accuracy of the determined safety status level information.

[0066] In an optional embodiment, the step of determining the security status level information based on the spatiotemporal information of multi-source data may be implemented in the following manner: The security status level information is determined by using the model to determine the security status level information based on the spatiotemporal information of multi-source data.

[0067] For example, spatiotemporal information from multiple data sources can be input into a safety status level determination model, and the output of the model can include safety status level information. Using a pre-trained model to determine safety status level information can thus complete complex computational tasks in a short period of time, more accurately determining safety status level information, reducing the probability of misjudgment and ensuring vehicle safety.

[0068] In one embodiment, the security status level information determination model may be trained in the following manner: Acquire multiple pieces of training sample data, each piece of training sample data including a multi-source data spatiotemporal information sample and a security status level information sample corresponding to the multi-source data spatiotemporal information sample; The neural network model is trained using the training sample data until the training end conditions are met, and a safety status level information determination model is obtained.

[0069] For example, the multi-source data spatiotemporal information sample in each piece of training sample data serves as the input data for the neural network model to be trained, and the security status level information sample corresponding to the multi-source data spatiotemporal information sample in each piece of training sample data serves as the target output data for the neural network model to be trained. The model can be stored locally on the electronic device and retrieved locally each time it is used, or it can be stored on a third-party platform and retrieved from the third party each time it is used, without specific limitations here.

[0070] In this way, the model can learn the inherent laws and patterns from the training sample data, improve the generalization ability, and use the new multi-source data spatiotemporal information to accurately determine the vehicle's safety status level information.

[0071] In one embodiment, the safety status level information determination model is further used to determine the friction coefficient of the road surface based on the spatiotemporal information of multi-source data.

[0072] Correspondingly, the security status level information determination model can be trained in the following ways: Acquire multiple pieces of training sample data, each piece of training sample data including a multi-source data spatiotemporal information sample, and a safety status level information sample and a friction coefficient sample corresponding to the multi-source data spatiotemporal information sample; The neural network model is trained using the training sample data until the training end conditions are met, and a safety status level information determination model is obtained.

[0073] For example, the multi-source data spatiotemporal information sample in each training sample data is the input data of the neural network model to be trained, and the safety status level information sample and the friction coefficient sample corresponding to the multi-source data spatiotemporal information sample in each training sample data are the target output data of the neural network model to be trained.

[0074] In this way, the accuracy of the determined friction coefficient can be improved by using the safety status level information to determine the model.

[0075] For example, the neural network model may be any one of an LSTM (Long Short-Term Memory) model and a GRU (Gated Recurrent Unit) model.

[0076] For example, the training end condition may include at least one of the following: the output value of the loss function of the model is less than or equal to a preset threshold, and the number of iterations reaches a preset threshold.

[0077] In some possible implementations, the vehicle control method provided by the present disclosure may further include: Before performing time alignment processing on the vehicle related information, filtering processing is performed on the vehicle related information to perform time alignment processing on the filtered vehicle related information.

[0078] For example, a Kalman filter method can be used to filter vehicle-related information, thereby improving data quality and availability, and providing a more reliable basis for subsequent safety status level information determination.

[0079] Figure 2 This is a flow chart of a vehicle control method according to an exemplary embodiment. Figure 2 , you can more clearly understand the implementation process of the vehicle control method provided by the present disclosure. Figure 2 As shown, the method may include steps S201 to S207.

[0080] In step S201 , vehicle motion information is acquired.

[0081] Among them, vehicle motion information includes vehicle speed, steering information, and vehicle body posture information.

[0082] In step S202, the road surface status information is identified by fusion of the laser radar and the camera.

[0083] The road surface status information includes road surface texture information and obstacle information.

[0084] In step S203, the contact state information between the tire and the ground is determined using data collected by sensors pre-installed inside the tire.

[0085] The contact state information between the tire and the ground includes the friction coefficient and the ground pressure.

[0086] In step S204, the vehicle motion information, the road surface state information, and the tire-ground contact state information are filtered.

[0087] In step S205, time alignment processing is performed on the filtered data to obtain spatiotemporal information of multi-source data.

[0088] In step S206, the security status level information is determined using a security status level information determination model and based on the spatiotemporal information of the multi-source data.

[0089] In step S207 , the chassis system of the vehicle is controlled according to the safety status level information.

[0090] This ensures the quality and reliability of multi-source spatiotemporal data. By integrating multi-dimensional data such as vehicle motion, road surface conditions, and tire-ground contact status, a vehicle's safety status can be more comprehensively and accurately assessed. Using this safety status information to control the vehicle's chassis system ensures a stable driving posture, enhancing occupant comfort and driving safety.

[0091] In addition, the specific implementation of the above steps S201 to S207 has been described in detail above, and the repeated content will not be repeated here.

[0092] In some possible implementations, the chassis system includes tire pressure, and the chassis system of the vehicle may be controlled according to the safety status level information in the following manner: Determine the target value for tire pressure adjustment based on the safety status level information; The tire pressure is adjusted according to the target value.

[0093] For example, the correspondence between safety status level information and optimal tire pressure can be pre-calibrated through testing. This correspondence can be expressed, for example, via a function or mapping table. By looking up this correspondence, the optimal tire pressure corresponding to the current safety status level information can be simply and quickly determined. The determined optimal tire pressure can then be used as the target value for tire pressure adjustment to adjust the tire pressure.

[0094] In some possible implementations, the chassis system includes a suspension, and the chassis system of the vehicle may be controlled according to the safety status level information in the following manner: Determine the target level of suspension stiffness adjustment based on the safety status level information; Based on the target level, the stiffness of the suspension is adjusted.

[0095] The target level represents the adjustment level of the suspension stiffness. For example, the correspondence between the safety status level information and the target level can be pre-calibrated through experiments. This correspondence can be represented, for example, by a function or mapping table. By searching this correspondence, the target level corresponding to the current safety status level information can be quickly and easily determined to adjust the suspension stiffness.

[0096] The following description takes the control of the vehicle suspension as an example. Figure 3 FIG. 1 is a flow chart of a vehicle control method according to an exemplary embodiment. Figure 3 As shown, step S103 may include step S1031 and step S1032.

[0097] In step S1031 , a target level for suspension stiffness adjustment is determined based on the safety status level information and reference information.

[0098] The higher the target level, the greater the suspension stiffness. Suspension stiffness is positively correlated with vehicle stability and negatively correlated with vehicle comfort.

[0099] In one embodiment, the greater the instability risk represented by the security status level information, the higher the target level.

[0100] The greater the instability risk represented by the safety status level information, the greater the possibility that the vehicle will lose stability. In order to improve the stability of the vehicle, a higher target level can be determined to increase the suspension stiffness.

[0101] The reference information includes at least one of a road scene and vehicle body posture information in the vehicle motion information.

[0102] Road scenarios include icy, snowy, wet, and dry roads. The probability of a vehicle skidding on icy or snowy roads is higher than on wet roads, and the probability of a vehicle skidding on wet roads is higher than on dry roads. Road scenarios can provide a certain degree of feedback on the risk of vehicle instability.

[0103] In one embodiment, the target level for icy or snowy road conditions is higher than the target level for slippery road conditions, which in turn is higher than the target level for dry road conditions. The higher the likelihood of slipping corresponding to a road condition, the greater the likelihood of vehicle instability. To improve vehicle stability, a higher target level may be determined to increase suspension stiffness.

[0104] The magnitude of changes in vehicle posture information (such as yaw and roll angles) can reflect the vehicle's lateral stability. Excessive yaw angle changes indicate a risk of vehicle instability; similarly, excessive roll angle changes also indicate a risk of vehicle instability. Therefore, vehicle posture information can, to a certain extent, provide feedback on the potential for vehicle instability.

[0105] In one embodiment, the greater the fluctuation amplitude of the vehicle posture information, the higher the target level. The greater the fluctuation amplitude of the vehicle posture information, the greater the possibility of vehicle instability. To improve vehicle stability, a higher target level can be determined to increase suspension stiffness.

[0106] By utilizing step S1031, based on the safety status level information and combined with at least one of the road scene and vehicle body posture information, the possibility of the vehicle losing stability can be more accurately determined, thereby improving the accuracy of the determined target level and achieving precise control of the vehicle.

[0107] In step S1032 , the suspension is controlled according to the target level.

[0108] For example, the stiffness of the vehicle suspension may be adjusted to a target level to ensure stability in vehicle operation.

[0109] To balance vehicle stability and ride comfort, you can set multiple target levels to adjust suspension stiffness for optimal performance. For example, within the target levels, level 1 < level 2 < level 3 < level 4 < level 5 < level 6. The higher the target level, the greater the adjusted suspension stiffness and the greater the vehicle stability.

[0110] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle is in a safe state, the road scene is a dry road, and the change value of the vehicle body posture information within a first preset time period is less than a first threshold, the target level is determined to be the first level.

[0111] For example, if the vehicle body posture information includes yaw angle and roll angle, the first threshold and first preset duration corresponding to the yaw angle, as well as the first threshold and first preset duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the first threshold for the yaw angle can be set to 1° and the first preset duration can be set to 10 seconds; the first threshold for the roll angle can be set to 1° and the first preset duration can be set to 5 seconds. For example, if the road surface is dry and the safety status level information indicates that the vehicle is in a safe state, such as the safety status level information is 0, if the yaw angle changes by less than 1° within 10 seconds and the roll angle changes by less than 1° within 5 seconds, it can be determined that the possibility of vehicle instability is extremely low, and the target level can be set to level 1, so as to utilize extremely low suspension stiffness to ensure driving safety and maintain a comfortable vehicle state.

[0112] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle is in a safe state, the road scene is a slippery road, and the change value of the vehicle body posture information within the second preset time period is less than the second threshold, the target level is determined to be the second level.

[0113] For example, if the vehicle body posture information includes yaw angle and roll angle, the second threshold and second preset time duration corresponding to the yaw angle, as well as the second threshold and second preset time duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the second threshold for the yaw angle can be set to 1.5° and the second preset time duration can be set to 8 seconds; the second threshold for the roll angle can be set to 1.5° and the second preset time duration can be set to 6 seconds. For example, if the road surface is slippery and the safety status level information indicates that the vehicle is in a safe state, such as the safety status level information is 0, if the yaw angle changes by less than 1.5° within 8 seconds and the roll angle changes by less than 1.5° within 6 seconds, it can be determined that the possibility of vehicle instability is relatively low, and the target level can be set to level 2, thereby utilizing relatively low suspension stiffness to ensure driving safety and vehicle comfort.

[0114] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle is at risk of instability, the road scene is a slippery road, and the change value of the vehicle body posture information within the third preset time period is less than the third threshold, the target level is determined to be the third level.

[0115] For example, if the vehicle body posture information includes yaw angle and roll angle, the third threshold and third preset duration corresponding to the yaw angle, and the third threshold and third preset duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the third threshold for the yaw angle can be set to 2, and the third preset duration can be set to 5 seconds; the third threshold for the roll angle can be set to 2°, and the third preset duration can be set to 3 seconds. For example, if the road surface is slippery and the safety status level information indicates a risk of vehicle instability, such as if the safety status level information is 1, if the yaw angle changes by less than 2° within 5 seconds and the roll angle changes by less than 2° within 3 seconds, the possibility of vehicle instability can be determined to be moderate, and the target level can be set to level 3, thereby utilizing relatively moderate suspension stiffness to ensure driving safety and reduce skidding.

[0116] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle is at risk of instability, the road scene is an icy or snowy road, and the change value of the vehicle body posture information within a fourth preset time period is less than a fourth threshold, the target level is determined to be the fourth level.

[0117] For example, if the vehicle body posture information includes yaw angle and roll angle, a fourth threshold and fourth preset duration corresponding to the yaw angle, as well as a fourth threshold and fourth preset duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the fourth threshold for the yaw angle can be set to 2.5° and the fourth preset duration can be set to 3 seconds; the fourth threshold for the roll angle can be set to 2.5° and the fourth preset duration can be set to 2 seconds. For example, if the road scene is icy or snowy and the safety status level information indicates a risk of vehicle instability, such as if the safety status level information is 1, if the yaw angle changes by less than 2.5° within 3 seconds and the roll angle changes by less than 2.5° within 2 seconds, it can be determined that the vehicle has a high probability of losing stability, and the target level can be set to level 4, thereby utilizing the relatively high suspension stiffness to ensure driving safety and further reduce skidding.

[0118] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle has become unstable, the road scene is an icy or snowy road, and the change value of the vehicle body posture information within the fifth preset time period is greater than the fifth threshold, the target level is determined to be the fifth level.

[0119] For example, if the vehicle body posture information includes yaw angle and roll angle, a fifth threshold value and a fifth preset duration corresponding to the yaw angle, as well as a fifth threshold value and a fifth preset duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the fifth threshold value for the yaw angle can be set to 3°, and the fifth preset duration can be set to 2 seconds; the fifth threshold value for the roll angle can be set to 3°, and the fifth preset duration can be set to 1 second. For example, if the road scene is icy or snowy and the safety status level information indicates vehicle instability, such as the safety status level information being 2, if the yaw angle changes by more than 3° within 2 seconds and the roll angle changes by more than 3° within 1 second, it can be determined that the vehicle is highly likely to lose stability, and the target level can be set to level 5 to adjust the suspension stiffness to a higher value to maximize vehicle stability.

[0120] In some possible implementations, in step S1031, determining a target level for suspension stiffness adjustment based on the safety status level information and the reference information includes: If the safety status level information indicates that the vehicle has become unstable, the vehicle body posture information continues to fluctuate within a sixth preset time period, and the fluctuation amplitude is greater than a sixth threshold, then the target level is determined to be the sixth level.

[0121] For example, if the vehicle body posture information includes yaw angle and roll angle, a sixth threshold value and a sixth preset duration corresponding to the yaw angle, as well as a sixth threshold value and a sixth preset duration corresponding to the roll angle, can be pre-set based on actual needs. For example, the sixth threshold value for both the yaw angle and the roll angle can be set to 3°, and the sixth preset duration can be set to 2 seconds. For example, if the safety status level information indicates vehicle instability, such as safety status level 2, if the yaw angle fluctuates continuously for 2 seconds with an amplitude exceeding 3°, and the roll angle also fluctuates continuously for 2 seconds with an amplitude exceeding 3°, the target level can be determined to be level 6, and the suspension stiffness can be adjusted to its maximum value in an attempt to recover the out-of-control vehicle.

[0122] In some possible implementations, the vehicle control method provided by the present disclosure may further include: The road scene is determined based on the road surface texture information in the road surface state information and the friction coefficient in the contact state information.

[0123] For example, collected road surface texture features can be matched against a pre-established library of road surface texture features to identify the most similar texture type. The friction coefficient can then be compared against the friction coefficient ranges for different road surface scenarios. By comprehensively considering the texture matching results and the friction coefficient ranges, the most likely road surface scenario can be determined. This allows for precise identification of the road surface scenario, providing reliable support for determining the target level.

[0124] In one embodiment, the road scene may be determined based on the road surface texture information in the road surface state information and the friction coefficient in the contact state information in the following manner: If the road surface texture information indicates that there is water on the road surface and the friction coefficient is within the first friction coefficient range, it is determined that the road surface scene is a slippery road surface.

[0125] For example, if the camera identifies a significant reflective area on the road (reflections from accumulated water), and the lidar detects signs of water coverage on the road surface, such as blurred texture, the road surface texture information can be determined to indicate accumulated water. The first friction coefficient range can be pre-set, for example, to 0.3 to 0.6.

[0126] In one embodiment, the road scene may be determined based on the road surface texture information in the road surface state information and the friction coefficient in the contact state information in the following manner: If the road surface texture information indicates that the road surface is covered with ice and snow, and the friction coefficient is within the second friction coefficient range, then the road scene is determined to be a snowy road.

[0127] For example, if the lidar and camera fusion identifies a large area of ​​white surface (snow) on the road surface, and the texture exhibits characteristics unique to ice and snow, such as a relatively smooth texture without noticeable graininess, while also failing to identify reflective areas similar to puddles that indicate slippery conditions but not ice and snow, the road surface texture information can be determined to indicate ice and snow. The second friction coefficient range can be pre-set, for example, to 0.1 to 0.3.

[0128] In one embodiment, the road scene may be determined based on the road surface texture information in the road surface state information and the friction coefficient in the contact state information in the following manner: If the road surface texture information indicates that the road surface is not covered with ice or snow and does not contain water, and the friction coefficient is within the third friction coefficient interval, then the road surface scene is determined to be a dry road surface.

[0129] For example, the third friction coefficient interval may be preset, for example, set to be greater than 0.6.

[0130] In some possible implementations, the vehicle control method provided by the present disclosure may further include at least one of the following: When a preset condition is met, displaying safety status level information and / or target level using a display device in the vehicle; When pre-set conditions are met, the safety status level information and / or the target level are announced by voice using the speakers in the vehicle; When preset conditions are met, the vibration device in the vehicle is used to feedback safety status level information and / or target level through vibration amplitude.

[0131] For example, the display device in the vehicle can be an onboard display screen. By using a display device, speaker, vibrator, or other device to allow users to obtain safety status level information and / or target level, users can promptly understand the vehicle's operating status, helping the driver to promptly identify potential hazards and take appropriate measures, thereby improving the riding experience.

[0132] In one embodiment, the preset condition may include: the security status level information is greater than a preset stability threshold.

[0133] For example, the stability threshold can be pre-set based on actual needs. If the safety status level information is greater than the preset stability threshold, it can be determined that the vehicle is more likely to lose stability. At this time, a prompt is given to the user to prepare for vehicle instability, which helps the driver to detect potential dangers in a timely manner and take measures.

[0134] In yet another embodiment, the preset condition may include: the target level is greater than a preset level threshold.

[0135] For example, a level threshold can be pre-set based on actual needs. If the target level is greater than the preset level threshold, it can be determined that the vehicle is likely to lose stability. The stiffness of the vehicle suspension will be adjusted to a higher value. At this time, a prompt will be given to the user to prepare for vehicle instability and reduced comfort, helping the driver to promptly identify potential dangers and take appropriate measures.

[0136] In another embodiment, the preset condition may include: a difference between the target level and the adjusted front suspension stiffness level is greater than a difference threshold.

[0137] For example, a difference threshold can be pre-set based on actual needs. If the difference between the target level and the pre-adjusted suspension stiffness level exceeds the difference threshold, it can be determined that the vehicle's suspension stiffness will undergo a significant adjustment, significantly impacting the ride experience. In this case, a user is prompted to prepare and avoid a sudden adjustment that could degrade the user experience.

[0138] Based on the same inventive concept, the present disclosure also provides a vehicle control device. Figure 4 FIG. 4 is a block diagram of a vehicle control device 400 according to an exemplary embodiment. Figure 4 , the vehicle control device 400 may include: An acquisition module 401 is configured to acquire vehicle-related information, wherein the vehicle-related information includes at least one of vehicle motion information, road surface state information, and tire-ground contact state information; The determination module 402 is configured to determine safety status level information of the vehicle based on the vehicle-related information; the safety status level information is used to indicate whether the vehicle is in a safe state or an unstable state of varying severity; The control module 403 is configured to control the chassis system of the vehicle according to the safety status level information.

[0139] In this technical solution, multi-dimensional data, including vehicle motion information, road surface conditions, and tire-ground contact status, is used to determine the vehicle's safety status, enabling a comprehensive and accurate assessment of the vehicle's safety status. This safety status information characterizes whether the vehicle is in a safe state or unstable states of varying severity. Controlling the vehicle's chassis system based on this safety status information effectively reduces the probability of vehicle instability, minimizing the risk of rollovers, skidding, and other dangerous situations, ensuring the safety of both occupants and the vehicle, and providing a smoother and more comfortable ride.

[0140] In some possible implementations, the determining module 402 includes: A first determining submodule is configured to perform time alignment processing on the vehicle related information to obtain spatiotemporal information of multi-source data; The second determining submodule is configured to determine the security status level information based on the spatiotemporal information of the multi-source data.

[0141] In some possible implementations, the second determining submodule is configured to determine the security status level information according to the multi-source data spatiotemporal information in the following manner: The security status level information is determined by using a security status level information determination model and based on the spatiotemporal information of the multi-source data.

[0142] In some possible implementations, the safety status level information determination model is further used to determine the friction coefficient of the road surface based on the spatiotemporal information of the multi-source data.

[0143] In some possible implementations, the security status level information determination model is trained in the following manner: Acquire multiple pieces of training sample data, each piece of the training sample data including a multi-source data spatiotemporal information sample and a security status level information sample corresponding to the multi-source data spatiotemporal information sample; The neural network model is trained using the training sample data until a training end condition is met, thereby obtaining the safety status level information determination model.

[0144] In some possible implementations, the first determining submodule is further configured to perform filtering processing on the vehicle-related information before performing time alignment processing on the vehicle-related information, so as to perform time alignment processing on the filtered vehicle-related information.

[0145] In some possible implementations, the vehicle motion information includes at least one of vehicle speed, steering information, and vehicle body posture information; the road surface state information includes at least one of road surface texture information and obstacle information; and the contact state information includes at least one of friction coefficient and ground pressure.

[0146] In some possible implementations, the chassis system includes a suspension; and the control module 403 includes: a third determining submodule, configured to determine a target level of suspension stiffness adjustment according to the safety status level information; wherein the target level is used to represent an adjustment level of the suspension stiffness; a control submodule, configured to control the suspension according to the target level; In some possible implementations, the third determining submodule includes: The fourth determination submodule is configured to determine a target level of suspension stiffness adjustment based on the safety status level information and reference information, wherein the reference information includes at least one of a road scene and vehicle body posture information in the vehicle motion information.

[0147] In some possible implementations, the fourth determining submodule is configured to determine a target level of suspension stiffness adjustment according to the safety status level information and reference information in the following manner: If the safety status level information indicates that the vehicle is in a safe state, the road scene is a dry road, and the change value of the vehicle body posture information within a first preset time period is less than a first threshold, then determining that the target level is the first level; If the safety status level information indicates that the vehicle is in a safe state, the road scene is a slippery road, and the change value of the vehicle body posture information within the second preset time period is less than a second threshold, then determining that the target level is the second level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is a slippery road, and the change value of the vehicle body posture information within a third preset time period is less than a third threshold, then determining that the target level is the third level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is icy or snowy, and the change in the vehicle posture information within a fourth preset time period is less than a fourth threshold, then determining that the target level is the fourth level; If the safety status level information indicates that the vehicle has become unstable, the road scene is an icy or snowy road, and the change value of the vehicle body posture information within a fifth preset time period is greater than a fifth threshold, then the target level is determined to be the fifth level; If the safety status level information indicates that the vehicle has become unstable, and the vehicle body posture information continues to fluctuate within a sixth preset time period, and the fluctuation amplitude is greater than a sixth threshold, then determining that the target level is the sixth level; The first level is lower than the second level, the second level is lower than the third level, the third level is lower than the fourth level, the fourth level is lower than the fifth level, and the fifth level is lower than the sixth level.

[0148] Among them, the first threshold, the second threshold, the third threshold, the fourth threshold, the fifth threshold, and the sixth threshold can be reasonably set according to the actual situation of the vehicle.

[0149] In some possible implementations, the apparatus 400 further includes at least one of a first prompt module, a second prompt module, and a third prompt module: The first prompt module is configured to display the safety status level information and / or the target level using a display device in the vehicle when a preset condition is met; The second prompt module is configured to, when the preset condition is met, use a speaker in the vehicle to voice broadcast the safety status level information and / or the target level; The third prompt module is configured to utilize a vibration device in the vehicle to provide feedback of the safety status level information and / or the target level through vibration amplitude when the preset conditions are met.

[0150] In some possible implementations, the preset condition includes at least one of the following: The safety status level information is greater than a preset stability threshold; The target level is greater than a preset level threshold; A difference between the target level and the adjusted front suspension stiffness level is greater than a difference threshold.

[0151] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0152] Figure 5FIG. 5 is a block diagram of a vehicle control device 500 according to an exemplary embodiment. Figure 5 , the apparatus 500 may include one or more of the following components: a first processing component 502 , a first memory 504 , a first power supply component 506 , a multimedia component 508 , an audio component 510 , a first input / output interface 512 , a sensor component 514 , and a communication component 516 .

[0153] The first processing component 502 generally controls the overall operation of the device 500, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The first processing component 502 may include one or more first processors 520 to execute instructions to perform all or part of the steps of the vehicle control method described above. Furthermore, the first processing component 502 may include one or more modules to facilitate interaction between the first processing component 502 and other components. For example, the first processing component 502 may include a multimedia module to facilitate interaction between the multimedia component 508 and the first processing component 502.

[0154] The first memory 504 is configured to store various types of data to support operations on the device 500. Examples of such data include instructions for any application or method operating on the device 500, contact data, phone book data, messages, pictures, videos, etc. The first memory 504 can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0155] The first power supply component 506 provides power to the various components of the device 500. The first power supply component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 500.

[0156] The multimedia component 508 includes a screen that provides an output interface between the device 500 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensors can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action. In some embodiments, the multimedia component 508 includes a front-facing camera and / or a rear-facing camera. When the device 500 is in an operating mode, such as a capture mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and the rear-facing camera can have a fixed optical lens system or have focal length and optical zoom capabilities.

[0157] The audio component 510 is configured to output and / or input audio signals. For example, the audio component 510 includes a microphone, which is configured to receive external audio signals when the device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the first memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 also includes a speaker for outputting audio signals.

[0158] The first input / output interface 512 provides an interface between the first processing component 502 and peripheral interface modules, such as a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0159] The sensor assembly 514 includes one or more sensors for providing various aspects of the status assessment of the device 500. For example, the sensor assembly 514 can detect the open / closed state of the device 500, the relative positioning of components, such as the display and keypad of the device 500. The sensor assembly 514 can also detect changes in the position of the device 500 or a component of the device 500, the presence or absence of user contact with the device 500, the orientation or acceleration / deceleration of the device 500, and temperature changes of the device 500. The sensor assembly 514 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 may also include a light sensor, such as an image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0160] The communication component 516 is configured to facilitate wired or wireless communication between the apparatus 500 and other devices. The apparatus 500 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 516 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 also includes a near-field communication module to facilitate short-range communication.

[0161] In an exemplary embodiment, the device 500 can be implemented by one or more application-specific integrated circuits, digital signal processors, digital signal processing devices, programmable logic devices, field programmable gate arrays, controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned vehicle control method.

[0162] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a first memory 504 including instructions. The instructions are executable by a first processor 520 of the apparatus 500 to implement the vehicle control method described above. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0163] Figure 6 FIG. 6 is a block diagram of a vehicle control device 600 according to an exemplary embodiment. For example, the device 600 may be provided as a server. Figure 6 The apparatus 600 includes a second processing component 622, which further includes one or more processors and a memory resource represented by a second memory 632 for storing instructions executable by the second processing component 622, such as an application. The application stored in the second memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the second processing component 622 is configured to execute the instructions to perform the vehicle control method described above.

[0164] The device 600 may further include a second power supply component 626 configured to perform power management of the device 600, a wired or wireless network interface 650 configured to connect the device 600 to a network, and a second input / output interface 658. The device 600 may operate based on an operating system stored in the memory 632.

[0165] Figure 7 FIG2 is a block diagram illustrating a vehicle 700 according to an exemplary embodiment. For example, vehicle 700 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or another type of vehicle. Vehicle 700 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

[0166] Reference Figure 7 Vehicle 700 may include various subsystems, such as an infotainment system 710, a perception system 720, a decision-making control system 730, a drive system 740, and a computing platform 750. Vehicle 700 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of vehicle 700 may be interconnected via wired or wireless means.

[0167] In some embodiments, the infotainment system 710 may include a communication system, an entertainment system, a navigation system, and the like.

[0168] Perception system 720 may include several sensors for sensing information about the environment surrounding vehicle 700. For example, perception system 720 may include a global positioning system (which may be a GPS system, a BeiDou system, or another positioning system), an inertial measurement unit, a laser radar, a millimeter-wave radar, an ultrasonic radar, and a camera.

[0169] The decision control system 730 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0170] The drive system 740 may include components that provide power to the vehicle 700. In one embodiment, the drive system 740 may include an engine, a power source, a transmission system, and wheels. The engine may be an internal combustion engine, an electric motor, an air compression engine, or a combination thereof. The engine is capable of converting energy provided by the power source into mechanical energy.

[0171] Some or all functions of the vehicle 700 are controlled by a computing platform 750. The computing platform 750 may include at least one third processor 751 and a third memory 752. The third processor 751 may execute instructions 753 stored in the third memory 752.

[0172] The third processor 751 may be any conventional processor, such as a commercially available CPU, or may include a graphics processor, a field programmable gate array, a system on chip, an application specific integrated circuit, or a combination thereof.

[0173] The third memory 752 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0174] In addition to the instructions 753 , the third memory 752 may also store data, such as road maps, route information, and vehicle location, direction, speed, etc. The data stored in the third memory 752 may be used by the computing platform 750 .

[0175] In the embodiment of the present disclosure, the third processor 751 can execute instructions 753 to complete all or part of the steps of the above-mentioned vehicle control method.

[0176] Some embodiments of the present disclosure also provide a chip system, such as Figure 8 As shown, the chip system includes at least one fourth processor 1301 and at least one interface circuit 1302. The fourth processor 1301 and the interface circuit 1302 can be interconnected via a line. For example, the interface circuit 1302 can be used to receive signals from other devices (such as the memory of an electronic device). For another example, the interface circuit 1302 can be used to send signals to other devices (such as the fourth processor 1301). Exemplarily, the interface circuit 1302 can read instructions stored in the memory and send the instructions to the fourth processor 1301. When the instructions are executed by the fourth processor 1301, the vehicle control device can execute the various steps in the above-mentioned embodiments. Of course, the chip system can also include other discrete components, and some embodiments of the present disclosure are not specifically limited to this.

[0177] In some embodiments of the present disclosure, the interface circuit 1302 can obtain data, program instructions and / or information from the internal storage area of ​​the chip system; it can also obtain data, program instructions and / or information from outside the chip system.

[0178] Optionally, the chip system may further include a memory for storing necessary computer programs and data.

[0179] In another exemplary embodiment, the present disclosure further provides a computer program product, which includes a computer program executable by a programmable device, and has a code portion for executing the above-mentioned vehicle control method when executed by the programmable device.

[0180] In another exemplary embodiment, the present disclosure further provides a computer-readable storage medium having computer program instructions stored thereon. When the program instructions are executed by a processor, the steps of the vehicle control method provided by the present disclosure are implemented.

[0181] Those skilled in the art will also appreciate that the various illustrative logic blocks and steps listed in the embodiments of the present application can be implemented by electronic hardware, computer software, or a combination of the two. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art can use various methods to implement the functions described for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present application.

[0182] Furthermore, the word "exemplary" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word exemplary is intended to present concepts in a concrete manner. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or." That is, unless otherwise specified or clear from the context, "X applies to A or B" is intended to mean any of the natural inclusive permutations. That is, if X applies to A; X applies to B; or X applies to both A and B, then "X applies to A or B" satisfies any of the aforementioned instances. Furthermore, the articles "a" and "an," as used in this application and the appended claims, are generally understood to mean "one or more," unless otherwise specified or clear from the context to refer to the singular form.

[0183] Likewise, although the present disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. The present disclosure includes all such modifications and variations and is limited only by the scope of the claims. With particular regard to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, terms used to describe such components are intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if not structurally equivalent to the disclosed structure. In addition, although particular features of the present disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms "include," "have," "have," "have," or variations thereof are used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term "comprising."

[0184] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

[0185] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

[0186] It should be understood that, unless otherwise specifically noted, the features of the various embodiments of the present disclosure described herein may be combined with each other. As used herein, the term "and / or" includes any one of the relevant listed items and any combination of any two or more thereof; similarly, "at least one of" includes any one of the relevant listed items and any combination of any two or more thereof.

[0187] Although terms such as "first", "second" and "third" may be used herein to describe various components, parts, regions, layers or sections, these components, parts, regions, layers or sections are not limited to these terms. On the contrary, these terms are only used to distinguish one component, part, region, layer or section from another component, part, region, layer or section. Therefore, without departing from the teachings of each example, the first component, part, region, layer or section mentioned in the examples described herein may also be referred to as the second component, part, region, layer or section. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" can explicitly or implicitly include at least one such feature. In the description herein, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A vehicle control method, characterized in that: include: Acquiring vehicle-related information, the vehicle-related information including at least one of vehicle motion information, road surface state information, and tire-ground contact state information; Determining safety status level information of the vehicle based on the vehicle-related information; the safety status level information is used to indicate whether the vehicle is in a safe state or an unstable state of varying severity; The chassis system of the vehicle is controlled according to the safety status level information.

2. The method according to claim 1, characterized in that Determining the safety status level information of the vehicle based on the vehicle-related information includes: Performing time alignment processing on the vehicle-related information to obtain multi-source data spatiotemporal information; The security status level information is determined based on the spatiotemporal information of the multi-source data.

3. The method according to claim 2, characterized in that The determining of the security status level information based on the spatiotemporal information of the multi-source data includes: The security status level information is determined by using a security status level information determination model and based on the spatiotemporal information of the multi-source data.

4. The method according to claim 3, characterized in that The safety status level information determination model is further used to determine the friction coefficient of the road surface based on the spatiotemporal information of the multi-source data.

5. The method according to claim 3, characterized in that The security status level information determination model is trained in the following way: Acquire multiple pieces of training sample data, each piece of the training sample data including a multi-source data spatiotemporal information sample and a security status level information sample corresponding to the multi-source data spatiotemporal information sample; The neural network model is trained using the training sample data until a training end condition is met, thereby obtaining the safety status level information determination model.

6. The method according to claim 2, characterized in that The method further comprises: Before performing time alignment processing on the vehicle related information, filtering processing is performed on the vehicle related information to perform time alignment processing on the filtered vehicle related information.

7. The method according to any one of claims 1 to 6, characterized in that The vehicle motion information includes at least one of vehicle speed, steering information, and vehicle body posture information; The road surface state information includes at least one of road surface texture information and obstacle information; The contact state information includes at least one of a friction coefficient and a ground contact pressure.

8. The method according to claim 1, characterized in that The chassis system includes a suspension; and controlling the chassis system of the vehicle according to the safety status level information includes: Determining a target level for suspension stiffness adjustment based on the safety status level information; wherein the target level is used to represent the adjustment level of the suspension stiffness; Based on the target level, the stiffness of the suspension is adjusted.

9. The method according to claim 8, characterized in that Determining a target level of suspension stiffness adjustment according to the safety status level information includes: A target level of suspension stiffness adjustment is determined based on the safety status level information and reference information, wherein the reference information includes at least one of a road scene and vehicle body posture information in the vehicle motion information.

10. The method according to claim 9, characterized in that The determining a target level of suspension stiffness adjustment according to the safety status level information and reference information includes: If the safety status level information indicates that the vehicle is in a safe state, the road scene is a dry road, and the change value of the vehicle body posture information within a first preset time period is less than a first threshold, then determining that the target level is the first level; If the safety status level information indicates that the vehicle is in a safe state, the road scene is a slippery road, and the change value of the vehicle body posture information within the second preset time period is less than a second threshold, then determining that the target level is the second level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is a slippery road, and the change value of the vehicle body posture information within a third preset time period is less than a third threshold, then determining that the target level is the third level; If the safety status level information indicates that the vehicle is at risk of instability, the road scene is icy or snowy, and the change in the vehicle posture information within a fourth preset time period is less than a fourth threshold, then determining that the target level is the fourth level; If the safety status level information indicates that the vehicle has become unstable, the road scene is an icy or snowy road, and the change value of the vehicle body posture information within a fifth preset time period is greater than a fifth threshold, then the target level is determined to be the fifth level; If the safety status level information indicates that the vehicle has become unstable, and the vehicle body posture information continues to fluctuate within a sixth preset time period, and the fluctuation amplitude is greater than a sixth threshold, then determining that the target level is the sixth level; The first level is lower than the second level, the second level is lower than the third level, the third level is lower than the fourth level, the fourth level is lower than the fifth level, and the fifth level is lower than the sixth level.

11. The method according to claim 8, characterized in that The method further comprises at least one of: When a preset condition is met, displaying the safety status level information and / or the target level using a display device in the vehicle; When the preset conditions are met, using a speaker in the vehicle to voice announce the safety status level information and / or the target level; When the preset conditions are met, the vibration device in the vehicle is used to feed back the safety status level information and / or the target level through vibration amplitude.

12. The method according to claim 11, characterized in that The preset condition includes at least one of the following: The safety status level information is greater than a preset stability threshold; The target level is greater than a preset level threshold; A difference between the target level and the adjusted front suspension stiffness level is greater than a difference threshold.

13. A vehicle control device, characterized in that: The device is used to implement the steps of the vehicle control method according to any one of claims 1 to 12.

14. A vehicle, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the executable instructions in the memory to implement the steps of the vehicle control method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vehicle control method according to any one of claims 1 to 12 are implemented.

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