Vehicle chassis control method, controller, vehicle, medium and program product

Abstract

The embodiment of the present invention provides a vehicle chassis control method, a controller, a vehicle, a medium and a program product, including: determining a weight value of a vehicle chassis system based on the driver's trust; the trust represents the degree of trust of the vehicle in the driver; and controlling the vehicle chassis system based on the weight value. By allocating a weight value of the vehicle chassis system based on the degree of trust of the vehicle in the driver and the degree of trust of the driver, the driving parameters of the vehicle chassis system can be adaptively adjusted based on the weight value of the vehicle chassis system, so that the operation of the vehicle chassis system fits the driver's driving habits to the greatest extent, and the safety of the vehicle and the driver is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control production, and in particular to a vehicle chassis control method, a controller, a vehicle, a computer-readable storage medium and a computer program product. Background Art

[0002] As assisted driving, pilot driving and other autonomous driving technologies become more popular, in actual driving, in addition to manual driving by the driver, there are also conditions where autonomous driving is used during driving. Currently, the vehicle chassis is controlled by preset rules, which is a relatively fixed control method and affects the safety of the vehicle and the driver. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a vehicle chassis control method, a vehicle controller, a vehicle, a computer-readable storage medium and a computer program product that overcome the above problems or at least partially solve the above problems.

[0004] In order to solve the above problems, an embodiment of the present invention discloses a vehicle chassis control method, comprising:

[0005] Determining a weight value of a vehicle chassis system based on the driver's trust; the trust represents the degree of trust the vehicle has in the driver;

[0006] Based on the weight values, the vehicle chassis system is controlled.

[0007] Optionally, before determining the weight value of the vehicle chassis system based on the driver's trust, the method further comprises:

[0008] Obtaining behavior information of the driver;

[0009] The trustworthiness of the driver is determined based on the behavior information.

[0010] Optionally, the behavior information includes: driver state information and / or driver operation information, and determining the driver's trustworthiness according to the behavior information includes:

[0011] Based on the driver status information and / or the driver operation information, the trust level of the driver is determined.

[0012] Optionally, determining the driver's trustworthiness based on the driver state information and / or the driver operation information includes:

[0013] The trust level of the driver is determined based on the driver status information and / or the driver operation information and weight information corresponding to the driver status information and / or the driver operation information.

[0014] Optionally, determining the trustworthiness of the driver based on the driver state information and / or the driver operation information and weight information corresponding to the driver state information and / or the driver operation information includes:

[0015] Determining weight information corresponding to the driver state information and / or the driver operation information and the preset table according to the driver state information and / or the driver operation information and the preset table;

[0016] The trust level of the driver is determined based on the driver status information and / or the driver operation information and weight information corresponding to the driver status information and / or the driver operation information.

[0017] Optionally, the operation information includes at least one of an intelligent driving system operation proficiency index, an intelligent driving system operation accuracy index and an intelligent driving system usage dependence index.

[0018] Optionally, the intelligent driving system operation proficiency index is determined by the following steps:

[0019] Obtaining the actual operation time of the intelligent driving system by the driver;

[0020] Based on the actual operation time and skilled operation time, determine the intelligent driving system operation proficiency index.

[0021] Optionally, the determining the intelligent driving system operation proficiency index by combining the actual operation time and the skilled operation time includes:

[0022] Determine the time difference between the actual operation time and the skilled operation time;

[0023] Based on the ratio of the time difference to the actual operation time, the intelligent driving system operation proficiency index is determined.

[0024] Optionally, the intelligent driving system operation accuracy index is determined by the following steps:

[0025] Obtaining the driver's manual operation accuracy and / or voice interaction accuracy of the intelligent driving system;

[0026] The intelligent driving system operation accuracy index is determined by combining the manual operation accuracy and / or the voice interaction accuracy.

[0027] Optionally, determining the intelligent driving system operation accuracy index in combination with the manual operation accuracy and / or the voice interaction accuracy includes:

[0028] Determine an accuracy sum value between the manual operation accuracy and the voice interaction accuracy;

[0029] Determine the accuracy product between the manual operation accuracy and the voice interaction accuracy;

[0030] Combine the accuracy sum value and the accuracy product to determine the intelligent driving system operation accuracy index.

[0031] Optionally, the intelligent driving system usage dependency index is determined in the following manner:

[0032] Determine driving time and intelligent driving time;

[0033] The intelligent driving system usage dependence index is determined by combining the driving time and the driving duration.

[0034] Optionally, the combining the driving time and the driving duration to determine the intelligent driving system usage dependence index includes:

[0035] The ratio between the intelligent driving time and the driving time is determined as the intelligent driving system usage dependence index.

[0036] Optionally, the method further comprises:

[0037] The trust level is determined according to a match between the driver's trust level and the historical trust level.

[0038] Optionally, determining the trust level according to a matching condition between the driver's trust level and a historical trust level includes:

[0039] In response to the driver's trust level not matching the historical trust level, the trust level is determined based on the historical trust level.

[0040] Optionally, determining the trust level according to the historical trust level includes:

[0041] determining an average of the historical trustworthiness;

[0042] The confidence level is determined using the average value.

[0043] Optionally, determining the weight value of the vehicle chassis system based on the driver's trust level includes:

[0044] Obtaining the last trust level of the driver;

[0045] Determining a current weight value corresponding to the driver's trustworthiness;

[0046] Calculating a change between the trust level and the last trust level;

[0047] The current weight value is adjusted based on the change value to obtain a weight value of the vehicle chassis system.

[0048] Optionally, adjusting the current weight value based on the change value to obtain a weight value of the vehicle chassis system includes:

[0049] Determine the absolute value of the change value as an adjustment value;

[0050] The current weight value is adjusted based on the change value and the adjustment value to obtain a weight value of the vehicle chassis system.

[0051] Optionally, the current weight value includes a manual weight value and an intelligent driving weight value; the sum of the manual weight value and the intelligent driving weight value is one; and adjusting the current weight value based on the change value and the adjustment value to obtain a weight value of the vehicle chassis system includes:

[0052] In response to the change value being positive, increasing the intelligent driving weight value and decreasing the manual weight value based on the adjustment value to obtain a weight value of the vehicle chassis system;

[0053] In response to the change value being negative, the manual weight value is increased and the intelligent driving weight value is reduced based on the adjustment value to obtain a weight value of the vehicle chassis system.

[0054] Optionally, controlling the vehicle chassis system based on the weight value includes:

[0055] Obtain manual input data and intelligent driving input data;

[0056] Combining the intelligent driving input data with the intelligent driving weight value to obtain a first control amount;

[0057] Combining the manual input data with the manual weight value to obtain a second control amount;

[0058] Determine a target control amount by combining the first control amount and the second control amount;

[0059] The vehicle chassis system is controlled based on the target control amount.

[0060] Optionally, the vehicle chassis system includes a steering system, a braking system, a driving system and a suspension system; and controlling the vehicle chassis system based on the target control amount includes:

[0061] At least one of the steering system, the braking system, the driving system, and the suspension system is controlled based on the target control amount.

[0062] Optionally, the method further comprises:

[0063] In response to triggering of a non-driving state, a chassis control weight value is determined based on the confidence level.

[0064] Optionally, the method further comprises:

[0065] identifying a driver state based on the behavior information;

[0066] The non-driving state is triggered based on the driver state.

[0067] Optionally, the behavior information includes driver posture data, steering wheel pressure data, pedal depth data and physiological data, and identifying the driver state based on the behavior information includes:

[0068] A driver state is identified based on at least one of the driver posture data, the steering wheel pressure data, the pedal depth data, and the physiological data.

[0069] Optionally, triggering the non-driving state based on the driver state includes:

[0070] In response to the posture corresponding to the driver posture data being a preset target posture, and / or,

[0071] In response to the steering wheel pressure data being greater than a preset pressure threshold, and / or,

[0072] In response to the pedal depth data being greater than a preset depth threshold, and / or,

[0073] In response to the physiological state corresponding to the physiological data being a preset physiological state, the non-driving state is triggered.

[0074] A vehicle controller comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the vehicle chassis control method as described above when executed by the processor.

[0075] A vehicle comprises the vehicle controller as described above.

[0076] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle chassis control method described above are implemented.

[0077] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the vehicle chassis control method described above are implemented.

[0078] The embodiments of the present invention include the following advantages:

[0079] The embodiment of the present invention determines the weight value of the vehicle chassis system based on the driver's trust; the trust represents the degree of trust the vehicle has in the driver; based on the weight value, the vehicle chassis system is controlled. By allocating the weight value of the vehicle chassis system based on the degree of trust the vehicle has in the driver, the driving parameters of the vehicle chassis system can be adaptively adjusted based on the weight value of the vehicle chassis system, so that the operation of the vehicle chassis system fits the driver's driving habits to the greatest extent, ensuring the safety of the vehicle and the driver, and because the degree of trust the driver has in the vehicle is fully considered, the sense of distrust caused to the driver by direct intelligent control of the vehicle can be avoided, thereby improving the driver's driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 is a flow chart of steps of an embodiment of a vehicle chassis control method of the present invention;

[0081] Figure 2 is a flowchart of another vehicle chassis control method embodiment of the present invention;

[0082] Figure 3 is a schematic diagram of a driver confidence determination process of another example of a vehicle chassis control method of the present invention;

[0083] Figure 4 is a schematic diagram of driver confidence updating in another example of a vehicle chassis control method of the present invention;

[0084] Figure 5 is a schematic diagram of non-driving state detection of another vehicle chassis control method example of the present invention;

[0085] Figure 6 is a control schematic diagram of another vehicle chassis control method example of the present invention;

[0086] Figure 7 It is a schematic diagram of the steps of another example of a vehicle chassis control method of the present invention. DETAILED DESCRIPTION

[0087] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0088] Reference Figure 1 , shows a flow chart of steps of an embodiment of a vehicle chassis control method of the present invention, and the vehicle chassis control method may specifically include the following steps:

[0089] Step 101, determining a weight value of a vehicle chassis system based on the driver's trust; the trust represents the degree of trust the vehicle has in the driver;

[0090] The vehicle can determine the degree of trust in the driver by identifying the driver's behavior, that is, determine the driver's trust level, and determine the weight value when controlling the vehicle chassis system based on the trust level.

[0091] Step 102: Control the vehicle chassis system based on the weight value.

[0092] Based on the adjusted weight value, the vehicle chassis system is controlled, and the vehicle chassis system performs corresponding operations to maintain stable driving of the vehicle under various vehicle operating conditions.

[0093] The embodiment of the present invention determines the weight value of the vehicle chassis system based on the driver's trust; the trust represents the degree of trust the vehicle has in the driver; based on the weight value, the vehicle chassis system is controlled. By allocating the weight value of the vehicle chassis system based on the degree of trust the vehicle has in the driver, the driving parameters of the vehicle chassis system can be adaptively adjusted based on the weight value of the vehicle chassis system, so that the operation of the vehicle chassis system fits the driver's driving habits to the greatest extent, ensuring the safety of the vehicle and the driver, and because the degree of trust the driver has in the vehicle is fully considered, the sense of distrust caused to the driver by direct intelligent control of the vehicle can be avoided, thereby improving the driver's driving experience.

[0094] Reference Figure 2 , shows a flow chart of steps of another vehicle chassis control method embodiment of the present invention, wherein the vehicle chassis control method may specifically include the following steps:

[0095] Step 201, obtaining the driver's behavior information;

[0096] In an embodiment of the present invention, various sensors such as cameras, pressure sensors, etc. may be installed on the vehicle; and various operations performed by the driver on the vehicle may also be recorded. The user's operations, the user's driving behavior during current driving, and historical driving behavior may be aggregated as driver behavior information. That is, the driver behavior information may be determined by the driver status monitoring system inside the vehicle using various detection equipment to collect and determine the driver's physiological information (heart rate, blood pressure, breathing rate, facial expressions, eye movements, etc.) and driving status information (the driver's proficiency in operating the steering wheel and smart devices, the accuracy and reliance on the smart driving system) over a long period of time. The driver behavior information may be characterized as information such as the driver's driving behavior or physiological state. Obtain the driver's behavior information.

[0097] Step 202, determining the driver's trust level based on the behavior information, wherein the trust level indicates the driver's trust level in the vehicle;

[0098] The driver's trust in the intelligent driving system on the vehicle, i.e. the driver's trust, can be determined through the sensor detection signals on the vehicle, the driver's operation and other driver behavior information. Among them, the intelligent driving system is a vehicle control system that controls the vehicle to perform various driving operations.

[0099] In an optional embodiment of the present invention, the behavior information includes: driver status information and / or driver operation information, and determining the driver's trust level based on the behavior information includes: determining the driver's trust level based on the driver status information and / or the driver operation information.

[0100] In an embodiment of the present invention, the behavior information includes driver state information and / or driver operation information. The driver state information represents the driver's current driving state to determine whether the driver remains focused on driving control of the vehicle. The driver operation information represents the driver's current driving behavior habits for the vehicle. Based on the driver state information and / or driver operation information, the driver's usage habits for the intelligent driving system can be determined, and the driver's trust can be determined.

[0101] In an optional embodiment of the present invention, determining the trust level of the driver based on the driver status information and / or the driver operation information includes: determining the trust level of the driver based on weight information corresponding to the driver status information and / or the driver operation information.

[0102] The driver's trustworthiness may be obtained by multiplying the weight information corresponding to the driver's state information and / or the driver's operation information with the driver's state information and / or the driver's operation information.

[0103] In an optional embodiment of the present invention, the trust level of the driver is determined based on the driver status information and / or the driver operation information and weight information corresponding to the driver status information and / or the driver operation information, including: determining the weight information corresponding to the driver status information and / or the driver operation information and a preset table based on the driver status information and / or the driver operation information and a preset table; determining the trust level of the driver based on the driver status information and / or the driver operation information and weight information corresponding to the driver status information and / or the driver operation information.

[0104] The preset table records the weight information corresponding to the driver status information and / or the driver operation information. After obtaining the driver status information and the driver operation information, the driver status information and the driver operation information can be used to find the weight information corresponding to the driver status information and the weight information corresponding to the driver operation information from the preset table by looking up the driver status information and the driver operation information. The driver status information is multiplied by the weight information corresponding to the driver status information, and the driver operation information is multiplied by the weight information corresponding to the corresponding driver operation information, and the weight values ​​of the two are added to obtain the driver's trust.

[0105] In an optional embodiment of the present invention, the operation information includes an intelligent driving system operation proficiency index, an intelligent driving system operation accuracy index and an intelligent driving system usage dependence index; the state information includes: driving state and non-driving state.

[0106] In an embodiment of the present invention, the operation information may include an intelligent driving system operation proficiency index, an intelligent driving system operation accuracy index, and an intelligent driving system use dependence index. The state information includes: driving state and non-driving state; thus, the driver's trust can be determined by operating the intelligent driving system operation proficiency index, and / or the intelligent driving system operation accuracy index, and / or the intelligent driving system use dependence index, and / or the non-driving state, and / or the driving state.

[0107] Among them, the intelligent driving system operation proficiency indicates the time fluctuation range of the driver's operation of the intelligent system. The intelligent driving system operation accuracy index indicates the accuracy of the driver's operation of the intelligent driving system. The intelligent driving system usage dependence index indicates the degree of driver's dependence on the intelligent driving system in the process of driving the vehicle. In addition, the corresponding impact of the non-driving state and the driving state can be expressed by the current driving state index. The severity of the driver's non-driving state or the degree of influence of the driving state.

[0108] In an optional embodiment of the present invention, the intelligent driving system operation proficiency index is determined by the following steps: obtaining the actual operation time of the intelligent driving system by the driver; and determining the intelligent driving system operation proficiency index by combining the actual operation time and the proficient operation time.

[0109] In an embodiment of the present invention, the actual operation time of the driver on the intelligent driving system can be obtained from the stored driving information. The actual operation time is the length of time the driver uses the intelligent driving system. It takes a certain amount of time for the driver to become proficient in using the intelligent driving system from the beginning. As the number of uses and the frequency of operation increase, their operation proficiency will gradually improve and stabilize.

[0110] When determining the proficiency index of the intelligent driving system, the proficient operation time can be determined. The proficient operation time is the theoretical time required to master the use of the intelligent driving system. The proficient operation time can be determined through data statistics or through relevant neural network models, such as using an expert system after multiple trainings. The actual operation time and the target operation time are combined to jointly determine the proficiency index of the intelligent driving system.

[0111] Specifically, the intelligent driving system operation proficiency index is determined by combining the actual operation time and the target operation time, including: determining the time difference between the actual operation time and the proficient operation time; and determining the intelligent driving system operation proficiency index based on the time ratio of the time difference to the actual operation time.

[0112] The time difference between the actual operation time and the skilled operation time can be calculated first, that is, the actual operation time minus the skilled operation time to get the time difference. Then calculate the time ratio between the time difference and the actual operation time, that is, divide the time difference by the actual operation time to get the time ratio. It can be seen that the time difference is the difference between the actual operation time and the target operation time. The time ratio is the ratio of the time difference to the actual operation time. The time ratio is used as the operation proficiency index of the intelligent driving system.

[0113] For example, it can be expressed by the formula:

[0114]

[0115] Among them, t0 is the skilled operation time; t s is the actual operation time, and its value range is (t0 +∞); P is the intelligent driving system operation proficiency index.

[0116] In an optional embodiment of the present invention, the intelligent driving system operation accuracy index is determined by the following steps: obtaining the driver's manual operation accuracy and / or voice interaction accuracy of the intelligent driving system; and determining the intelligent driving system operation accuracy index in combination with the manual operation accuracy and / or the voice interaction accuracy.

[0117] The manual operation accuracy rate is the accuracy rate of the driver's manual operation of the intelligent driving system. The voice interaction accuracy rate is the accuracy rate of the driver's instructions issued through the voice interaction system. The operation accuracy index of the intelligent driving system is determined by combining the manual operation accuracy rate and / or the voice interaction accuracy rate.

[0118] Specifically, the intelligent driving system operation accuracy index is determined by combining the manual operation accuracy and / or the voice interaction accuracy, including: determining the accuracy sum value between the manual operation accuracy and the voice interaction accuracy; determining the accuracy product between the manual operation accuracy and the voice interaction accuracy; and determining the intelligent driving system operation accuracy index by combining the accuracy sum value and the accuracy product.

[0119] For the calculation of the intelligent driving system operation accuracy index, the accuracy sum between the manual operation accuracy and the voice interaction accuracy can be calculated, that is, the manual operation accuracy and the voice interaction accuracy are added to obtain the accuracy sum. When calculating the accuracy product between the manual operation accuracy and the voice interaction accuracy, the manual operation accuracy and the voice interaction accuracy are multiplied to obtain the accuracy product. Then, the accuracy difference between the accuracy sum and the accuracy product is obtained, that is, the accuracy sum minus the accuracy product to obtain the accuracy difference. The accuracy difference is determined as the intelligent driving system operation accuracy index.

[0120] It can be expressed by the formula:

[0121] A=P(M)+P(N)-P(M)×P(N)

[0122] Among them, P(M) is the manual operation accuracy, P(N) is the voice interaction accuracy, and A is the intelligent driving system operation accuracy index.

[0123] The intelligent driving system operation accuracy index A is between 0 and 1, and the index will increase as the driver's proficiency in using the intelligent driving system improves.

[0124] In an optional embodiment of the present invention, the intelligent driving system usage dependence index is determined in the following manner: determining the driving time and the intelligent driving time; and determining the intelligent driving system usage dependence index in combination with the driving time and the driving time.

[0125] The intelligent driving time is the time that the intelligent driving system is used in a single driving trip. The driving time is the time required to complete the entire journey in a single driving trip. The intelligent driving time and driving time can be determined by recording the driving process. The intelligent driving system usage dependence index is determined by the intelligent driving time and driving time.

[0126] Specifically, determining the intelligent driving system usage dependence index by combining the intelligent driving time and the driving time includes: determining an intelligent driving ratio between the intelligent driving time and the driving time as the intelligent driving system usage dependence index.

[0127] The intelligent driving ratio between the intelligent driving time and the driving time can be calculated, that is, the intelligent driving time is divided by the driving time to obtain the intelligent driving ratio; the intelligent driving ratio is the ratio of the intelligent driving time to the driving time. The intelligent driving ratio is determined as the intelligent driving system usage dependence index.

[0128] It can be expressed by the formula, namely;

[0129]

[0130] Among them, t i T is the duration of intelligent driving. i is the driving time, and D is the proficiency index and the dependency index of the intelligent driving system.

[0131] In addition, the current driving state index may also be determined based on the non-driving behavior, such as determining the current driving state index corresponding to the non-driving behavior type.

[0132] The driver's behavior can be identified based on the driver's behavior information, and the type of non-driving behavior can be determined, that is, the non-driving behavior type. The difference is that the non-driving behavior type corresponds to different current driving state indexes. When the driver is in a mild non-driving state, such as turning his head to look outside the car to relieve neck pain and then continuing to drive, taking his hands off the steering wheel to adjust his posture and then immediately holding the steering wheel, etc., the non-driving behavior at this time has a low impact on the safety of the vehicle and the driver, so the above current driving state index is low. When the driver is in a serious non-driving state, such as severe fatigue and about to fall asleep, taking his hands off the steering wheel for a long time, etc., the driving behavior at this time has a serious impact on the safety of the vehicle and the driver. Specific data can be identified and judged through fuzzy control. The current driving state index corresponding to the current driving state index is used as the current driving state index this time.

[0133] In an optional embodiment of the present invention, after obtaining the intelligent driving system operation proficiency index, the intelligent driving system operation accuracy index, the intelligent driving system use dependence index, and the current driving state index, the trust can be determined based on the intelligent driving system operation proficiency index, the intelligent driving system operation accuracy index, the intelligent driving system use dependence index, and the current driving state index. The proficiency index, and / or the accuracy index, and / or the dependence index, and / or the weight value corresponding to the current driving state index can be determined; the driver's trust can be determined in combination with the proficiency index, and / or the accuracy index, and / or the dependence index, and / or the current driving state and the weight value of the corresponding.

[0134] Weights can be pre-set for the vehicle's intelligent driving system, and weight values ​​corresponding to the intelligent driving system operation proficiency index, and / or the intelligent driving system operation accuracy index, and / or the intelligent driving system usage dependence index, and / or the current driving status index can be determined, that is, the weight value corresponding to the intelligent driving system operation proficiency index, the weight value corresponding to the intelligent driving system operation accuracy index, the weight value corresponding to the intelligent driving system usage dependence index, and the weight value corresponding to the current driving status index can be determined.

[0135] Then, the intelligent driving system operation proficiency index, intelligent driving system operation accuracy index, intelligent driving system use dependence index, and current driving state index are combined with the corresponding weight values ​​to determine the driver's trust. For example, the product of the proficiency index and the weight value corresponding to the proficiency index, the product of the accuracy index and the weight value corresponding to the accuracy index, the product of the dependence index and the weight value corresponding to the dependence index, and the product of the current driving state index and the weight value corresponding to the current driving state index are added together to determine the driver's trust.

[0136] It can be expressed by the formula:

[0137] T=λ1*P+λ2*A+λ3*D+λ4*S

[0138] Among them, λ1, λ2, λ3 and λ4 represent the weights of the intelligent driving system operation proficiency index, the intelligent driving system operation accuracy index, the intelligent driving system usage dependence index and the current driving state index respectively. S is the current driving state index.

[0139] In addition, the driving confidence can be updated in real time, so that the vehicle chassis can be accurately controlled. In an optional embodiment of the present invention, the method further includes: determining the confidence according to the matching of the driver's confidence with the historical confidence.

[0140] The historical trust can be obtained from relevant historical data. The historical trust is the trust of past drivers in the vehicle. It is determined whether the driver's trust matches the historical trust and whether the currently determined driver's trust is consistent with the historical change trend. Determine the driver's trust.

[0141] In an optional embodiment of the present invention, determining the trust level based on a match between the driver's trust level and the historical trust level includes: in response to a mismatch between the driver's trust level and the historical trust level, determining the trust level based on the historical trust level.

[0142] When the driver's trust does not match the historical trust, it indicates that there is an error in the current driver's trust. To ensure driving safety, the driver's trust can be updated based on the historical trust in response to the mismatch between the driver's trust and the historical trust.

[0143] Specifically, determining the trust level according to the historical trust levels includes: determining an average value of the historical trust levels; and using the average value to determine the driver's trust level.

[0144] The historical trust level may include trust levels at multiple different periods. The average value of the historical trust levels may be calculated, and the average value may be used to determine the current driver's trust level.

[0145] Further, when the driver's confidence matches the historical confidence, in response to the driver's confidence matching the historical confidence, the step of determining the weight value of the vehicle chassis system based on the driver's confidence is executed to perform subsequent chassis control.

[0146] For example, the calculation process of driving trust can refer to Figure 3 , the trust factor includes the practice index, accuracy index, dependence index, and current driving state index. The above formulas are used to calculate the practice index, accuracy index, dependence index, and current driving state index to obtain driving trust. After obtaining the trust index, the size of the trust is compared with the previous trust change curve to determine whether the trust in the non-driving state is caused by other external factors. If the size of the trust is obviously inconsistent with the change of the previous trust index, the average value of the previous K trusts is taken as the current trust, the trust result is updated and recorded, and the current trust value is sent to the vehicle control system for control weight allocation. The weight of the vehicle control right is equal to the trust value.

[0147] For the corresponding update process, please refer to Figure 4 The driver’s trust is calculated based on the record of each time the driver uses the intelligent driving system, and the trust data is saved and updated each time. Through long-term records, it is analyzed whether the driver’s driving ability has improved. By comparing the previous trust change curve with the current driver’s trust, it is determined whether the current driver’s driving ability needs to be updated. At the same time, the current driver’s driving trust is recorded.

[0148] Step 203, identifying the driver status based on the driver behavior information;

[0149] The current state of the driver, that is, the driver state, can be identified from the driver behavior information. The identification can be performed by image analysis or sensor, which is not specifically limited in the embodiment of the present invention.

[0150] Step 204, triggering the non-driving state based on the driver state;

[0151] Based on the driver state, it is determined whether a non-driving pattern will exist, thereby triggering a non-driving state.

[0152] In an optional embodiment of the present invention, the driver behavior information includes driver posture data, steering wheel pressure data, pedal depth data and physiological data, and identifying the driver status based on the driver behavior information includes: identifying the driver status based on at least one of the driver posture data, the steering wheel pressure data, the pedal depth data and the physiological data.

[0153] Driver posture data can represent the driver's facial expressions and body movements. Steering wheel pressure data can represent the driver's grip on the steering wheel. Pedal depth data can represent the depth of the driver's brake pedal, accelerator pedal and other operating pedals. Physiological data refers to various physiological information of the driver.

[0154] The driver is identified based on at least one of the driver posture data, the steering wheel pressure data, the pedal depth data and the physiological data, and the driver state is determined.

[0155] Specifically, the step of triggering the non-driving state based on the driver state includes: in response to the posture corresponding to the driver posture data being a preset target posture, and / or, in response to the steering wheel pressure data being greater than a preset pressure threshold, and / or, in response to the pedal depth data being greater than a preset depth threshold, and / or, in response to the physiological state corresponding to the physiological data being a preset physiological state, triggering the non-driving state.

[0156] The non-driving state can be triggered when it is monitored that at least one of the following conditions is met: the posture corresponding to the driver's posture data is a preset target posture, the steering wheel pressure data is greater than a preset pressure threshold, the pedal depth data is greater than a preset depth threshold, and the physiological data corresponds to a preset physiological state.

[0157] Among them, the preset target posture, the preset pressure threshold, and the preset physiological state can all be determined according to actual conditions, and the embodiment of the present invention does not limit the specific content.

[0158] For example, the detection of non-driving state can refer to Figure 5 The infrared camera is used to monitor the driver's facial expressions and body movements, etc. The steering wheel pressure sensor is used to monitor the degree of the driver's hand grip on the steering wheel. The brake and accelerator pedal pressure sensors are used to monitor the pressure of the driver's feet on the corresponding pedals. The physiological signal monitoring sensor is used to monitor various physiological information of the driver.

[0159] The information obtained by the above monitoring equipment is combined to determine whether the driver's current driving state is in a non-driving state. If it is a non-driving state, the current driver's trust value is calculated. If not, the driver state monitoring system continues to monitor the driver's driving state.

[0160] Step 205, in response to the triggering of the non-driving state, determining a chassis control weight value based on the trust level;

[0161] In response to the triggering of the non-driving state, the chassis control weight value can be determined based on the driver's trust to determine the degree of intervention of the intelligent driving system, so that autonomous driving can be more in line with the driver's driving habits.

[0162] In an optional embodiment of the present invention, the step of determining the chassis control weight value based on the driver's trust includes: obtaining the driver's last trust; determining the current weight value corresponding to the driver's trust; calculating the change value between the trust and the last trust; adjusting the current weight value based on the change value to obtain the weight value of the vehicle chassis system.

[0163] The driver's last trust can be obtained. And the current weight value corresponding to the currently calculated driver's trust is determined. The change value between the current weight value and the last trust can be calculated. The value obtained by subtracting the driver's trust from the last trust can be used as the change value. The change value can be positive or negative. A positive value indicates that the driver's trust in the automatic driving control of the intelligent driving system has increased compared to the previous driving process. Conversely, a negative value indicates that the driver's trust in the automatic driving control of the intelligent driving system has decreased compared to the previous driving process. The current weight value is adjusted based on the change value to obtain the weight value of the vehicle chassis system.

[0164] Specifically, the step of adjusting the current weight value based on the change value to obtain the weight value of the vehicle chassis system includes: determining the absolute value of the change value as the adjustment value; adjusting the current weight value based on the change value and the adjustment value to obtain the weight value of the vehicle chassis system.

[0165] The adjustment value is determined based on the degree of change, and the adjustment value can be determined based on the absolute value of the change value. For example, if the unit change value corresponds to a unit value, the number of units is determined based on the absolute value of the change value, and then the adjustment value is determined by the sum of the several unit values. For example, the unit change value is 1, the unit value corresponding to the unit change value is 0.1, and the absolute value of the change value is 3, that is, the adjustment value is 0.3.

[0166] The current weight value is adjusted using the adjustment value to obtain the current vehicle chassis control weight value.

[0167] Further, the current weight value includes a manual weight value and an intelligent driving weight value; the sum of the manual weight value and the intelligent driving weight value is one; the step of adjusting the current weight value based on the change value and the adjustment value to obtain the weight value of the vehicle chassis system includes: in response to the change value being positive, increasing the intelligent driving weight value and decreasing the manual weight value based on the adjustment value to obtain the weight value of the vehicle chassis system; in response to the change value being negative, increasing the manual weight value and decreasing the intelligent driving weight value based on the adjustment value to obtain the weight value of the vehicle chassis system.

[0168] The manual weight value is the control weight value corresponding to the manual driving operation. The intelligent driving weight value is the control weight value corresponding to the automatic driving operation. The sum of the manual weight value and the intelligent driving weight value is one. When the change value is positive, the automatic driving control can be used more. That is, the intelligent driving weight value can be increased by the adjustment value and the manual weight value can be reduced by the corresponding adjustment value to obtain the weight value of the vehicle chassis system. Conversely, when the change value is negative, the manual driving control can be used more. That is, the manual weight value can be increased by the adjustment value and the intelligent driving weight value can be reduced by the corresponding adjustment value to obtain the weight value of the vehicle chassis system.

[0169] Step 206: Control the vehicle chassis system based on the weight value.

[0170] The current chassis control weight value is used to control the vehicle chassis system so that the vehicle chassis system of the vehicle can operate more safely and stably.

[0171] In an optional embodiment of the present invention, the step of controlling the vehicle chassis system based on the weight value includes: taking manual input data and intelligent driving input data; combining the intelligent driving input data with the intelligent driving weight value to obtain a first control quantity; combining the manual input data with the manual weight value to obtain a second control quantity; combining the first control quantity and the second control quantity to determine a target control quantity; and controlling the vehicle chassis system based on the target control quantity.

[0172] When performing actual control, manual input data and intelligent driving input data can be received. Manual input data refers to the control input amount generated by the driver's manual operation. Intelligent driving input data refers to the control input amount generated when the intelligent driving system is connected to control.

[0173] The manual input data can be combined with the manual weight value, such as multiplying the manual input data with the manual weight value to obtain a first control amount. The first control amount is a control amount corresponding to the manual operation part. The manual input data is combined with the manual weight value, such as multiplying the intelligent driving input data with the intelligent driving weight value to obtain a second control amount. The second control amount is a control amount corresponding to the intelligent driving operation part. Finally, the first control amount and the second control amount are combined, such as adding the first control amount and the second control amount to obtain a target control amount. The vehicle chassis system is controlled based on the target control amount.

[0174] Specifically, the chassis includes a steering system, a braking system, a drive system and a suspension system; the step of controlling the vehicle chassis system based on the target control amount includes: controlling at least one of the steering system, the braking system, the drive system and the suspension system based on the target control amount.

[0175] The target control amount can be used to control the steering system, braking system, drive system and suspension system respectively, thereby achieving control of the chassis.

[0176] You can refer to Figure 6 After obtaining the vehicle control weight, the intelligent chassis system will combine the vehicle control weight and the values ​​of four indexes (proficiency index, accuracy index, dependence index and current driving state index) to assign different weights to the four chassis systems (drive system, brake system, steering system and suspension system). The steering control system mainly controls the steering angle, which is mainly related to the non-driving state of the driver's hand operating the steering wheel. The brake control system mainly controls the brake pedal opening, which is mainly related to the non-driving state of the driver's foot operating the brake pedal. The drive control system mainly controls the accelerator pedal opening, which is mainly related to the non-driving state of the driver's foot operating the accelerator pedal. The intelligent suspension control system mainly controls the stiffness and damping of the suspension, which is mainly related to other non-driving states such as driver distraction and fatigue driving. After completing the control of the above chassis systems, the on-board controller will monitor the status of the entire vehicle and feedback the current status of the chassis system to the intelligent chassis system. When it is found that the system does not reach the standard state, the intelligent chassis will be fine-tuned again until the expected control target is achieved. Thereby ensuring the safety of the vehicle and the driver.

[0177] In order to make the embodiments of the present invention clear to those skilled in the art, Figure 7 As shown, an example is used to illustrate:

[0178] The driver status monitoring system inside the vehicle uses various detection equipment to collect long-term information on the driver's physiological information (heart rate, blood pressure, breathing rate, facial expressions, eye movements, etc.) and driving status information (driver's proficiency in operating the steering wheel and smart devices, accuracy and reliance on the intelligent driving system), and obtains the basic value of the driver's trust in the vehicle system through data analysis. At the same time, the in-vehicle monitoring system will monitor the driver's driving status in real time. When the driver's non-driving status (hands off the steering wheel, feet off the brake and accelerator pedals, turning to appreciate the scenery outside the car, playing with mobile phones, listening to music, fatigue driving sleep, closing eyes to rest, etc.) is identified, the vehicle control system will calculate the driver's trust based on the current driver's non-driving status information, and compare and analyze it with the driver's previous trust to determine the current driver's trust. Then, the vehicle's control weight is allocated according to the current driver's trust. The control weight is mainly allocated to the four subsystems of the intelligent chassis (drive system, braking system, steering system and suspension system). Then, by adjusting the chassis parameters, each chassis actuator completes the action and monitors and feedbacks the entire vehicle status and driver status. When the driver is in a long-term non-driving state or the non-driving state gradually affects the safety of the vehicle and the driver, the on-board controller will adjust the vehicle control weights in real time according to the current state, and ultimately ensure the safety of the vehicle and the driver.

[0179] The embodiment of the present invention determines the driver's trust based on the driver's behavior information; identifies the driver's state based on the driver's behavior information; triggers the non-driving state based on the driver's state; determines the chassis control weight value according to the driver's trust in response to the triggering of the non-driving state; and performs chassis control based on the chassis control weight value. The driver's trust in the vehicle's intelligent control system is fully considered to avoid the driver's distrust caused by direct intelligent control, improve the driver's driving experience, and for non-driving state scenarios, the driver's trust index in the vehicle is determined in combination with the current driver's driving state index, vehicle operation proficiency, accuracy of the intelligent control system, and dependence on the intelligent driving system, and gradually improves the driver's comfort in driving the vehicle. The control weights of the four major systems of the intelligent chassis are allocated according to the trust index, and the driving parameters of the chassis system are adaptively adjusted to fit the driver's driving habits to the greatest extent, ensuring the safety of the vehicle and the driver.

[0180] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0181] An embodiment of the present invention further discloses a vehicle controller, comprising a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the vehicle chassis control method as described above when executed by the processor.

[0182] An embodiment of the present invention further discloses a vehicle, comprising the vehicle controller as described above.

[0183] The embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the vehicle chassis control method described above are implemented.

[0184] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the steps of the vehicle chassis control method described above are implemented.

[0185] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0186] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0187] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0188] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0189] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0190] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0191] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0192] The vehicle chassis control method, a vehicle controller, a vehicle, a computer-readable storage medium and a computer program product provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A vehicle chassis control method, characterized in that: include: Determine the weight value of the vehicle chassis system based on the driver's trust; Based on the weight value, controlling the vehicle chassis system; Wherein, determining the weight value of the vehicle chassis system based on the driver's trust level includes: Obtaining the last trust level of the driver; Determining a current weight value corresponding to the driver's trustworthiness; Calculating a change between the trust level and the last trust level; The current weight value is adjusted based on the change value to obtain a weight value of the vehicle chassis system.

2. The method according to claim 1, characterized in that Before determining the weight value of the vehicle chassis system based on the driver's trust, the method includes: Obtaining behavior information of the driver; The trustworthiness of the driver is determined based on the behavior information.

3. The method according to claim 2, characterized in that The behavior information includes: driver status information and / or driver operation information, and determining the driver's trustworthiness based on the behavior information includes: Based on the driver status information and / or the driver operation information, the trust level of the driver is determined.

4. The method according to claim 3, characterized in that The determining the trustworthiness of the driver based on the driver state information and / or the driver operation information includes: The trust level of the driver is determined based on the driver status information and / or the driver operation information and weight information corresponding to the driver status information and / or the driver operation information.

5. The method according to claim 4, characterized in that The determining the trust level of the driver based on the driver state information and / or the driver operation information and weight information corresponding to the driver state information and / or the driver operation information includes: Determining weight information corresponding to the driver state information and / or the driver operation information and the preset table according to the driver state information and / or the driver operation information and the preset table; The trust level of the driver is determined based on the driver status information and / or the driver operation information, and weight information corresponding to the driver status information and / or the driver operation information.

6. The method according to claim 3 or 4, characterized in that: The operation information includes at least one of an intelligent driving system operation proficiency index, an intelligent driving system operation accuracy index, and an intelligent driving system usage dependence index.

7. The method according to claim 6, characterized in that The intelligent driving system operation proficiency index is determined by the following steps: Obtaining the actual operation time of the intelligent driving system by the driver; Based on the actual operation time and skilled operation time, determine the intelligent driving system operation proficiency index.

8. The method according to claim 7, characterized in that The determining of the intelligent driving system operation proficiency index by combining the actual operation time and the skilled operation time includes: Determine the time difference between the actual operation time and the skilled operation time; Based on the ratio of the time difference to the actual operation time, the intelligent driving system operation proficiency index is determined.

9. The method according to claim 6, characterized in that The intelligent driving system operation accuracy index is determined by the following steps: Obtaining the driver's manual operation accuracy and / or voice interaction accuracy of the intelligent driving system; The intelligent driving system operation accuracy index is determined by combining the manual operation accuracy and / or the voice interaction accuracy.

10. The method according to claim 9, characterized in that The determining of the intelligent driving system operation accuracy index by combining the manual operation accuracy and / or the voice interaction accuracy includes: Determine an accuracy sum value between the manual operation accuracy and the voice interaction accuracy; Determine the accuracy product between the manual operation accuracy and the voice interaction accuracy; Combine the accuracy sum value and the accuracy product to determine the intelligent driving system operation accuracy index.

11. The method according to claim 6, characterized in that The intelligent driving system usage dependency index is determined in the following manner: Determine driving time and intelligent driving time; The intelligent driving system usage dependence index is determined by combining the driving time and the driving duration.

12. The method according to claim 11, characterized in that The combining the driving time and the driving time to determine the intelligent driving system usage dependence index includes The ratio between the intelligent driving time and the driving time is determined as the intelligent driving system usage dependence index.

13. The method according to claim 1, characterized in that The method further comprises: The trust level is determined according to a match between the driver's trust level and the historical trust level.

14. The method according to claim 13, characterized in that The determining the trust level according to the matching between the trust level of the driver and the historical trust level includes: In response to the driver's trust level not matching the historical trust level, the trust level is determined based on the historical trust level.

15. The method according to claim 14, characterized in that The determining the trust level according to the historical trust level includes: determining an average of the historical trustworthiness; The confidence level is determined using the average value.

16. The method according to claim 1, characterized in that The step of adjusting the current weight value based on the change value to obtain a weight value of the vehicle chassis system includes: Determine the absolute value of the change value as an adjustment value; The current weight value is adjusted based on the change value and the adjustment value to obtain a weight value of the vehicle chassis system.

17. The method according to claim 16, characterized in that The current weight value includes a manual weight value and an intelligent driving weight value; the sum of the manual weight value and the intelligent driving weight value is one; The step of adjusting the current weight value based on the change value and the adjustment value to obtain a weight value of the vehicle chassis system includes: In response to the change value being positive, increasing the intelligent driving weight value and decreasing the manual weight value based on the adjustment value to obtain a weight value of the vehicle chassis system; In response to the change value being negative, the manual weight value is increased and the intelligent driving weight value is reduced based on the adjustment value to obtain a weight value of the vehicle chassis system.

18. The method according to claim 17, characterized in that The controlling the vehicle chassis system based on the weight value comprises: Obtain manual input data and intelligent driving input data; Combining the intelligent driving input data with the intelligent driving weight value to obtain a first control amount; Combining the artificial input data with the artificial weight value to obtain a second control amount; Determine a target control amount by combining the first control amount and the second control amount; The vehicle chassis system is controlled based on the target control amount.

19. The method according to claim 18, characterized in that The vehicle chassis system includes a steering system, a braking system, a drive system and a suspension system; The controlling the vehicle chassis system based on the target control amount comprises: At least one of the steering system, the braking system, the driving system, and the suspension system is controlled based on the target control amount.

20. The method according to claim 2, characterized in that The method further comprises: In response to triggering of a non-driving state, a chassis control weight value is determined based on the confidence level.

21. The method according to claim 20, characterized in that The method further comprises: Identify driver status based on behavior information; The non-driving state is triggered based on the driver state.

22. The method according to claim 21, characterized in that The behavior information includes driver posture data, steering wheel pressure data, pedal depth data and physiological data, and the identifying the driver state based on the behavior information includes: A driver state is identified based on at least one of the driver posture data, the steering wheel pressure data, the pedal depth data, and the physiological data.

23. The method according to claim 22, characterized in that The triggering of the non-driving state based on the driver state comprises: In response to the posture corresponding to the driver posture data being a preset target posture, and / or, In response to the steering wheel pressure data being greater than a preset pressure threshold, and / or, In response to the pedal depth data being greater than a preset depth threshold, and / or, In response to the physiological state corresponding to the physiological data being a preset physiological state, the non-driving state is triggered.

24. A controller, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the steps of the vehicle chassis control method according to any one of claims 1 to 23 when executed by the processor.

25. A vehicle, characterized in that: Comprising a controller as claimed in claim 24.

26. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle chassis control method according to any one of claims 1 to 23 are implemented.

27. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the steps of the vehicle chassis control method according to any one of claims 1 to 23.

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