Multi-sensor fusion vehicle stability control method, control system and vehicle
By using multi-sensor fusion technology, high-precision perception and advance control of the road surface ahead are achieved, solving the problem of insufficient perception of road adhesion coefficient, improving vehicle stability and user experience, and meeting functional safety requirements.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA FAW CO LTD
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-02
AI Technical Summary
In existing intelligent driving systems, the perception range and accuracy of road adhesion coefficient are insufficient, the perception results are not intuitive, and the stability control response is lagging, which limits the improvement of the user's driving experience.
Employing multi-sensor fusion technology, combining lidar, millimeter-wave radar, vision cameras, and wheel speed sensors, the system calculates the road surface adhesion coefficient ahead through deep fusion and filtering algorithms, and displays the adhesion level and reliability on the vehicle's instrument panel, allowing for early control of the braking and drive systems to avoid the risk of skidding.
It achieves high-precision, long-distance perception of the road surface ahead, enabling advance control of the braking and drive systems, improving vehicle stability and user driving experience, meeting the functional safety ASIL D level requirements, and avoiding the risk of skidding.
Smart Images
Figure CN122126249A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle technology, and in particular to a multi-sensor fusion vehicle stability control method, control system, and vehicle. Background Technology
[0002] With the development of vehicle technology, the market demands increasingly higher vehicle stability. Vehicle stability is influenced by many factors, among which the road surface adhesion coefficient (TBC) is a core parameter affecting vehicle braking, driving, and steering stability. Its accurate identification and early prediction are crucial for ensuring stable vehicle operation. Currently, intelligent driving systems still have many shortcomings in vehicle stability-related technologies, such as insufficient range and accuracy of TBC perception, unintuitive presentation of perception results, and delayed stability control response. These limitations hinder improvements in the user's driving experience. Summary of the Invention
[0003] The present invention aims to solve the technical problems existing in the above-mentioned related technologies, and proposes a multi-sensor fusion vehicle stability control method, control system and vehicle, which can improve the user's driving experience.
[0004] The multi-sensor fusion vehicle stability control method according to a first aspect of the present invention is characterized by comprising the following steps: Acquire information from multiple sensors; Based on information from multiple sensors, the road surface adhesion coefficient at each point within a preset road area in front of the vehicle is calculated. The preset road area is divided into multiple road segments along the vehicle's driving direction, and the average coefficient of each road segment is calculated to obtain the average coefficient of each road segment. When there is at least one low-adjacent road segment with an average coefficient not greater than a preset warning value among multiple road segments, the vehicle instrument panel is controlled to issue a warning and execute a pre-control step; the pre-control step includes: controlling the master cylinder to establish pre-braking pressure and controlling the drive system to reduce output torque.
[0005] The multi-sensor fusion vehicle stability control method according to the first aspect of the present invention has at least the following technical effects: when a low-friction road section is detected ahead, the control system controls the master cylinder to build up braking pressure in advance, reduces the gap between the brake disc and the friction pads, improves the response speed of the braking system, and prepares for subsequent braking; at the same time, the drive system reduces the output torque in advance to avoid the risk of slippage, and avoids the excessive torque of the drive wheels causing serious slippage after the vehicle enters the low-friction road surface, which is conducive to improving the user's driving experience.
[0006] According to some embodiments of the present invention, the step of acquiring multi-sensor information includes: controlling lidar and millimeter-wave radar to acquire road surface contour information, controlling a vision camera to acquire road surface image information, and controlling wheel speed sensors and inertial measurement units to acquire vehicle motion state information.
[0007] According to some embodiments of the present invention, the control method further includes the following steps: Based on the preset threshold range, each average coefficient is divided into corresponding adhesion levels, where different adhesion levels are associated with different predefined colors; The steps for controlling the vehicle's instruments to issue a warning include: Control the vehicle's instrument panel to sequentially display the predefined colors corresponding to the adhesion levels of the multiple road segments, arranged in order of proximity to the vehicle's current location.
[0008] According to some embodiments of the present invention, the control method further includes the following steps: Based on the multi-sensor information, the credible quantification value of each average coefficient is calculated; The distance range of each road segment, the adhesion level, and the reliable quantification value are sent to the vehicle's instrument panel for display.
[0009] According to some embodiments of the present invention, the pre-control step includes: The current vehicle speed is obtained, and based on the current vehicle speed, the average coefficient of the low-adhesion road section, and the speed-adhesion coefficient-distance mapping relationship calibrated by the actual vehicle, the intervention distance to apply intervention before reaching the low-adhesion road section is determined. Monitor the real-time distance between the vehicle and the low-adhesion road section. When the real-time distance is less than or equal to the intervention distance, execute the steps of establishing pre-braking pressure in the master cylinder and controlling the drive system to reduce output torque.
[0010] According to some embodiments of the present invention, the pre-control step includes: Based on the current vehicle speed, the average coefficient of the low-adhesion road section, the vehicle speed-adhesion coefficient-braking pressure mapping relationship and the vehicle speed-adhesion coefficient-torque reduction coefficient mapping relationship calibrated on the actual vehicle, the target pressure value for establishing pre-braking pressure and the torque reduction coefficient for reducing output torque are determined.
[0011] According to some embodiments of the present invention, the pre-control step includes: Obtain the adhesion coefficients of the left and right sides of the low-adhesion road section; Calculate the difference between the adhesion coefficient of the left road surface and the adhesion coefficient of the right road surface; When the difference is greater than the preset difference value, the left and right wheel distribution ratio for establishing pre-braking pressure and reducing output torque is adjusted.
[0012] According to some embodiments of the present invention, the control method further includes the following steps: The system monitors multi-sensor signals and fusion results in real time. When a sensor failure or abnormal fusion result is detected, it controls redundant sensors to replace the faulty ones and issues a fault warning.
[0013] According to a second aspect of the present invention, a vehicle stability control system employing the above-described multi-sensor fusion vehicle stability control method is provided, the control system comprising: The multi-sensor perception fusion module is used to acquire information from multiple sensors and calculate the road surface adhesion coefficient. The road surface adhesion coefficient segment prompt module is used to receive the data output by the multi-sensor perception fusion module, calculate the average coefficient and reliable quantitative value of each road segment, and send the distance to the vehicle range, adhesion level and reliable quantitative value of each road segment to the vehicle instrument for display. The warning module is used to monitor the average coefficients. When there is at least one low-level road segment with an average coefficient not greater than the preset warning value among multiple road segments, the vehicle instrument will issue a warning. The vehicle stability control module is used to control the master cylinder to build up pre-braking pressure and control the drive system to reduce output torque; The functional safety protection module is used to monitor multi-sensor signals in real time. When a sensor failure is detected, it controls the redundant sensor to replace it and issues a fault prompt.
[0014] The vehicle stability control system according to a second aspect of the present invention has at least the following technical effects: by applying the above-described multi-sensor fusion vehicle stability control method, vehicle stability is ensured and the user's driving experience is improved.
[0015] A vehicle according to a third aspect embodiment of the present invention includes the vehicle stability control system described above.
[0016] The vehicle according to the third aspect of the present invention has at least the following technical effects: by setting the above-described vehicle stability control system, vehicle stability is ensured and the user's driving experience is improved.
[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description
[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of a multi-sensor fusion vehicle stability control method according to an embodiment of the present invention; Figure 2 This is a flowchart of the steps for controlling the vehicle's instrument panel to issue a warning, according to one embodiment of the present invention; Figure 3 This is a flowchart of the pre-control steps in one embodiment of the present invention; Figure 4 This is a flowchart of the pre-control steps in one embodiment of the present invention. Detailed Implementation
[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.
[0020] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, "several" means one or more, "multiple" means two or more, "greater than," "less than," "exceeding," etc., are understood to exclude the stated number, while "above," "below," "within," etc., are understood to include the stated number. If "first" or "second" is used, it is only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.
[0021] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.
[0022] Understandably, existing technologies largely focus on identifying the coefficient of friction of the road surface the vehicle is currently traveling on, resulting in a limited predictive range for the road surface ahead. This fails to provide sufficient lead time and accurate parameter support for vehicle stability control. Furthermore, existing perception methods often rely on single sensors, making them ill-suited for complex driving scenarios. In existing technologies, the identification results of the road surface coefficient of friction are often simply displayed numerically, making it difficult for drivers to quickly and intuitively assess the differences in grip across different road sections ahead. Moreover, the reliability of the perception results is not quantified, leaving drivers and intelligent driving control systems unable to judge the reliability of the perception data, potentially leading to control decision errors due to perception errors. In existing technologies, vehicle stability control typically only activates after the vehicle enters a low-friction surface and exhibits a tendency to slip, representing passive control with a delayed response. This inability to proactively avoid slippage risks hinders the improvement of the user's driving experience. Therefore, this invention proposes a multi-sensor fusion-based vehicle stability control method, control system, and vehicle, which can enhance the user's driving experience.
[0023] The following is for reference. Figures 1 to 4 This invention describes a multi-sensor fusion vehicle stability control method, a control system, and a vehicle according to embodiments of the present invention.
[0024] The multi-sensor fusion vehicle stability control method of the first aspect of the present invention, referring to... Figure 1 This includes the following steps: Step S100: Acquire information from multiple sensors.
[0025] Step S200: Based on multi-sensor information, calculate the road surface adhesion coefficient at each point within a preset road range ahead of the vehicle. The preset road range can be 0-200m from the vehicle's current position. The calculation of the road surface adhesion coefficient employs a deep fusion algorithm combining early and late fusion, incorporating an improved Sage-Husa noise estimator and an unscented Kalman filter algorithm. This process fuses multi-source sensor information, suppressing interference from individual sensors and predicting the road surface adhesion coefficient at each point within 0-200m ahead. This achieves long-distance, high-precision perception of the road surface adhesion coefficient within this range, providing sufficient lead time for vehicle stability control.
[0026] Step S300: Divide the preset road area into multiple road segments along the vehicle's travel direction, and calculate the average coefficient of each road segment by averaging the adhesion coefficient of each road segment. One method for dividing the road segments is to divide the road surface 0-200m ahead of the vehicle into 10 segments, each segment consisting of 20m segments.
[0027] Step S500: Determine whether all average coefficients are greater than the preset warning value. The preset warning value can be 0.5.
[0028] In step S600, if at least one average coefficient is less than or equal to a preset warning value, that is, when there is at least one low-friction road segment with an average coefficient not greater than the preset warning value among multiple road segments, the vehicle's instrument panel issues a warning and the pre-control step S700 is executed. Road segments with an average coefficient greater than the preset warning value are not prone to slippage and are considered passable sections; therefore, excessive vehicle intervention and warnings are unnecessary to avoid redundant information interfering with the driver's normal driving. Road segments with an average coefficient less than the preset warning value are prone to slippage.
[0029] In step S700, the vehicle's braking system is controlled to establish pre-braking pressure, and the vehicle's drive system is controlled to reduce output torque. When a low-friction section is detected ahead, the control system controls the master cylinder to establish braking pressure in advance, reducing the gap between the brake disc and friction pads, improving the braking system's response speed, and preparing for subsequent braking. At the same time, the drive system reduces output torque in advance to avoid the risk of slippage, preventing excessive torque on the drive wheels after the vehicle enters the low-friction surface, which would lead to severe slippage and improve the user's driving experience.
[0030] In step S800, if all average coefficients are greater than the preset warning value, and the vehicle has completely passed the low-adjacent section, the vehicle stability control module sends a control termination command, the braking system releases the pre-built pressure and returns to normal; the drive system gradually restores normal output torque to ensure that the vehicle's power performance is not affected; and the warning issued by the vehicle's instrument panel is turned off.
[0031] In some embodiments of the present invention, step S100 specifically includes: controlling a lidar and millimeter-wave radar to acquire road surface contour information, controlling a vision camera to acquire road surface image information, and controlling a wheel speed sensor and an inertial measurement unit (IMU) to acquire vehicle motion state information. By employing multi-sensor deep fusion technology (lidar + millimeter-wave radar + vision camera + wheel speed sensor + IMU) combined with an improved filtering algorithm, accurate prediction of the road surface adhesion coefficient 0-200m in front of the vehicle can be achieved. In this embodiment, all sensors are general-purpose sensors, eliminating the need to rely on expensive dedicated sensors to obtain the adhesion coefficient, which helps reduce costs and facilitates widespread application.
[0032] In some embodiments of the present invention, the multi-sensor fusion vehicle stability control method further includes the following steps: Step S410: Based on a preset threshold range, each average coefficient is divided into corresponding adhesion levels, where different adhesion levels are associated with different predefined colors. The specific level division is as follows (can be adjusted according to the actual vehicle model calibration): Level 1 (poor grip, red): average coefficient ≤ 0.3 (icy or snowy roads). Level 2 (average grip, yellow): 0.3 < average coefficient ≤ 0.5 (thin snow, waterlogged roads); Level 3 (good grip, light green): 0.5 < average coefficient ≤ 0.7 (wet road surface); Level 4 (Good grip, green): 0.7 < average coefficient ≤ 0.85 (dry asphalt road surface). Level 5 (Excellent grip, green): Average coefficient > 0.85 (dry cement road surface).
[0033] In step S600, refer to Figure 2 The steps to control the vehicle's instrument panel to issue a warning include: Step S610: Control the vehicle's instrument panel to display predefined colors corresponding to the grip levels of multiple road segments in order of distance from the vehicle's current location, from closest to furthest. Specifically, generate a "tire grip heatmap" for the area 0-200m ahead on the vehicle's instrument panel. The vertical axis of the heatmap represents distance (0-200m), and the horizontal axis represents the vehicle's width. Different colors correspond to grip levels: poor grip (level 1) is displayed in red, average grip (level 2) in yellow, good grip (level 3), and excellent grip and above (levels 4-5) in green. The heatmap is updated every 100ms based on real-time average coefficient data, allowing the driver to monitor changes in grip ahead in real time. Displayed in color blocks, the driver can quickly and intuitively judge the grip differences between different road segments ahead.
[0034] In some embodiments of the present invention, the multi-sensor fusion vehicle stability control method further includes the following steps: Step S420: Based on multi-sensor information, calculate the quantified value of the reliability of each average coefficient (calculated based on sensor accuracy and fusion algorithm error model quantization, with a value range of 0-100%).
[0035] In step S430, the distance range to the vehicle, adhesion level, and confidence level of each road segment are sent to the vehicle's instrument panel for display. The data is output to the instrument panel in text form, such as "40-60m ahead, road surface adhesion coefficient 0.4 (Level 2), confidence level 80%", updating the information for each road segment in real time so that the driver can promptly obtain road conditions.
[0036] In some embodiments of the present invention, reference is made to... Figure 3 Step S700 includes: Step S710: Obtain the current vehicle speed. Based on the current vehicle speed, the average coefficient of the low-adhesion road section, and the speed-adhesion coefficient-distance mapping relationship calibrated on the actual vehicle, determine the intervention distance to apply intervention before reaching the low-adhesion road section. The speed-adhesion coefficient-distance mapping relationship is obtained through actual vehicle calibration: through actual vehicle testing, different vehicle speeds and different adhesion coefficients of road conditions are simulated, vehicle driving data is collected, and the advance control distance required for the vehicle to safely pass through the low-adhesion road surface under different conditions is collected. A recursive least squares algorithm is used to fit the data, forming a speed-adhesion coefficient-distance correlation map to ensure the rationality of the advance control distance. In the map, the higher the vehicle speed and the lower the adhesion coefficient, the larger the intervention distance. The baseline intervention distance is 60m (at a vehicle speed of 60km / h and an adhesion coefficient of 0.5). For example, at a vehicle speed of 80km / h and an adhesion coefficient of 0.3, the intervention distance is adjusted to 90m; at a vehicle speed of 40km / h and an adhesion coefficient of 0.4, the intervention distance is adjusted to 50m.
[0037] In addition, the real vehicle calibration also generates brake pre-build pressure targets ("vehicle speed-adhesion coefficient-brake pressure" mapping relationship map) and drive torque reduction targets ("vehicle speed-adhesion coefficient-torque reduction coefficient" mapping relationship map) under different operating conditions, providing data support for stability control.
[0038] Step S720: Based on the current vehicle speed, the average coefficient of the low-adhesion section, the vehicle speed-adhesion coefficient-braking pressure mapping relationship calibrated on the actual vehicle, and the vehicle speed-adhesion coefficient-torque reduction coefficient mapping relationship calibrated on the actual vehicle, determine the target pressure value for establishing pre-braking pressure and the torque reduction coefficient for reducing output torque. The lower the adhesion coefficient and the higher the vehicle speed, the greater the pre-braking pressure and the greater the torque reduction coefficient, i.e., the greater the torque reduction magnitude. The torque reduction target is determined based on the maximum driving torque that the current road surface can provide and the torque reduction coefficient. The torque reduction coefficient can be dynamically adjusted according to the adhesion coefficient; the smaller the adhesion coefficient, the lower the lower limit of the torque reduction coefficient.
[0039] By combining real vehicle data under different vehicle speeds and adhesion coefficients, the range of dynamic pre-pressure build-up and the range of drive torque reduction coefficient are calibrated to ensure that pre-pressure build-up can improve response speed without causing unnecessary braking drag; torque reduction can avoid slippage while maximizing the vehicle's basic power requirements; passive control is transformed into active pre-control, solving the problems of lag and low precision in existing control technologies, and effectively avoiding the risk of slippage on low-adhesion roads.
[0040] Step S721: Monitor the real-time distance between the vehicle and the low-adjacent road section; Step S722: Determine if the real-time distance is greater than the intervention distance. Step S723: If the real-time distance is less than or equal to the intervention distance, execute the steps of controlling the master cylinder to establish pre-braking pressure and controlling the drive system to reduce output torque. Determining the intervention distance based on different operating conditions allows for timely intervention while avoiding premature intervention that could compromise comfort.
[0041] In some embodiments of the present invention, reference is made to... Figure 4 Step S700 includes: Step S730: Obtain the adhesion coefficients of the left and right sides of the low-adhesion road section; Step S731: Calculate the difference between the adhesion coefficient of the left road surface and the adhesion coefficient of the right road surface; Step S732: Determine whether the difference is greater than a preset difference value; Step S733: When the difference is greater than the preset difference value, adjust the left and right wheel distribution ratio to establish pre-braking pressure and reduce output torque.
[0042] By comparing the coefficients of friction of the road surface on the left and right sides in front of the vehicle, when the difference in the coefficients of friction between the two sides is greater than a preset difference value (calibrated through real vehicle testing, the preset difference value is 0.2 for some models), it is determined to be a split road surface, and the location range of the split road surface is marked. When a split road surface is detected ahead, the vehicle stability control module further combines the difference in the coefficients of friction of the left and right sides to adjust the left and right wheel distribution ratio of braking pre-build pressure and drive torque reduction. It applies a greater reduction in torque and pre-build pressure to the wheel on the side with the lower coefficient of friction to avoid single-wheel slippage that would cause the vehicle to veer off course, further improving vehicle stability.
[0043] In some embodiments of the present invention, the multi-sensor fusion vehicle stability control method further includes the following steps: Step S900: Monitor multi-sensor signals and fusion results in real time. When a sensor failure or abnormal fusion result is detected, control the redundant sensor to replace it and issue a fault prompt.
[0044] It is understandable that the road adhesion coefficient sensing signal in an intelligent driving system is a core safety signal. Existing technologies lack a protection mechanism that meets the ASIL D functional safety level for this type of signal, and lack multiple redundancy designs and comprehensive fault detection, isolation, and recovery mechanisms. This poses risks such as sensing signal failure and false alarms, which can easily lead to safety accidents such as vehicle loss of control. This embodiment meets the ASIL D functional safety level requirements through redundancy design and fault diagnosis, solving the problems of short sensing range, low accuracy, and insufficient safety level in existing technologies.
[0045] The vehicle stability control system of the second aspect of the present invention applies the above-described multi-sensor fusion vehicle stability control method, and the control system includes: The multi-sensor perception fusion module is used to acquire information from multiple sensors and calculate the road surface adhesion coefficient. This module includes a lidar unit, millimeter-wave radar, a vision camera, wheel speed sensors, and an inertial measurement unit (IMU). The lidar and millimeter-wave radar acquire distance and contour information of the road surface 0-200m ahead of the vehicle; the vision camera acquires images of road surface texture, water accumulation, and snow accumulation; and the wheel speed sensors and IMU acquire motion parameters such as the vehicle's current wheel speed and acceleration. The multi-sensor perception fusion module uses a fusion algorithm to predict the road surface adhesion coefficient, identify split road surfaces (road surfaces with significantly different adhesion coefficients on the left and right sides), calculate the reliability of the perception results, and output a perception signal that meets ASIL D level requirements.
[0046] The road surface adhesion coefficient segmentation prompt module is used to receive data output by the multi-sensor perception fusion module, including adhesion coefficient, distance and confidence data. The road is divided into segments of 20m each. The average coefficient of each segment is calculated and the adhesion level is determined. The confidence quantification value of each segment is calculated and the corresponding distance vehicle range, adhesion level and confidence quantification value of each segment are sent to the vehicle instrument for segmented display.
[0047] The warning module monitors the average coefficients. When at least one low-adhesion road segment with an average coefficient not exceeding a preset warning value exists among multiple road segments, the vehicle's instrument panel issues a warning. It monitors the road surface adhesion coefficient from 0-200m ahead in real time. When a low-adhesion road surface (average coefficient ≤ 0.5) is detected, the vehicle's instrument panel issues a warning (i.e., it generates and displays a "tire grip heat map" for the 0-200m ahead), updating in real time. If no low-adhesion road surface is detected, the heat map is not displayed to avoid redundant interference.
[0048] The vehicle stability control module receives the adhesion coefficient, distance data, and current vehicle speed output by the multi-sensor perception fusion module. Combined with the speed-adhesion coefficient-distance map calibrated from the real vehicle test, it determines the intervention distance, target pressure value, and torque reduction coefficient. It then sends control commands to the braking system and drive system respectively, controlling the master cylinder to establish pre-braking pressure and controlling the drive system to reduce output torque, thus achieving advance control.
[0049] The functional safety assurance module employs a dual-sensor redundancy design, signal fault diagnosis algorithm, and fault tolerance mechanism to monitor multi-sensor signals in real time, identify and isolate faults, and control redundant sensors to replace sensors when a sensor fault is detected, while issuing a fault warning to ensure the reliability of core signals such as adhesion coefficient and distance, meet the functional safety ASIL D level requirements, and avoid control errors caused by signal failure.
[0050] By applying the aforementioned multi-sensor fusion vehicle stability control method, vehicle stability is ensured and the user's driving experience is improved.
[0051] A vehicle according to a third aspect embodiment of the present invention includes the vehicle stability control system described above. By setting up the vehicle stability control system, vehicle stability is ensured and the user's driving experience is improved.
[0052] The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.
Claims
1. A vehicle stability control method based on multi-sensor fusion, characterized in that, Includes the following steps: Acquire information from multiple sensors; Based on information from multiple sensors, the road surface adhesion coefficient at each point within a preset road area in front of the vehicle is calculated. The preset road area is divided into multiple road segments along the vehicle's driving direction, and the average coefficient of each road segment is calculated to obtain the average coefficient of each road segment. When there is at least one low-level road segment among multiple road segments whose average coefficient is not greater than the preset warning value, the vehicle instrument will issue a warning and execute a pre-control procedure. The pre-control steps include: controlling the master cylinder to establish pre-braking pressure and controlling the drive system to reduce output torque.
2. The vehicle stability control method based on multi-sensor fusion according to claim 1, characterized in that: The steps for acquiring multi-sensor information include: controlling lidar and millimeter-wave radar to collect road surface contour information, controlling a vision camera to collect road surface image information, and controlling wheel speed sensors and inertial measurement units to collect vehicle motion state information.
3. The vehicle stability control method based on multi-sensor fusion according to claim 1, characterized in that: The control method further includes the following steps: Based on the preset threshold range, each average coefficient is divided into corresponding adhesion levels, where different adhesion levels are associated with different predefined colors; The steps for controlling the vehicle's instruments to issue a warning include: Control the vehicle's instrument panel to sequentially display the predefined colors corresponding to the adhesion levels of the multiple road segments, arranged in order of proximity to the vehicle's current location.
4. The vehicle stability control method based on multi-sensor fusion according to claim 3, characterized in that: The control method further includes the following steps: Based on the multi-sensor information, the credible quantification value of each average coefficient is calculated; The distance range of each road segment, the adhesion level, and the reliable quantification value are sent to the vehicle's instrument panel for display.
5. The vehicle stability control method based on multi-sensor fusion according to claim 1, characterized in that: The pre-control steps include: The current vehicle speed is obtained, and based on the current vehicle speed, the average coefficient of the low-adhesion road section, and the speed-adhesion coefficient-distance mapping relationship calibrated by the actual vehicle, the intervention distance to apply intervention before reaching the low-adhesion road section is determined. Monitor the real-time distance between the vehicle and the low-adhesion road section. When the real-time distance is less than or equal to the intervention distance, execute the steps of establishing pre-braking pressure in the master cylinder and reducing output torque in the drive system.
6. The vehicle stability control method based on multi-sensor fusion according to claim 5, characterized in that: The pre-control steps include: Based on the current vehicle speed, the average coefficient of the low-adhesion road section, the vehicle speed-adhesion coefficient-braking pressure mapping relationship and the vehicle speed-adhesion coefficient-torque reduction coefficient mapping relationship calibrated on the actual vehicle, the target pressure value for establishing pre-braking pressure and the torque reduction coefficient for reducing output torque are determined.
7. The vehicle stability control method based on multi-sensor fusion according to claim 1, characterized in that: The pre-control steps include: Obtain the adhesion coefficients of the left and right sides of the low-adhesion road section; Calculate the difference between the adhesion coefficient of the left road surface and the adhesion coefficient of the right road surface; When the difference is greater than the preset difference value, the left and right wheel distribution ratio for establishing pre-braking pressure and reducing output torque is adjusted.
8. The vehicle stability control method based on multi-sensor fusion according to claim 1, characterized in that: The control method further includes the following steps: The system monitors multi-sensor signals and fusion results in real time. When a sensor failure or abnormal fusion result is detected, it controls redundant sensors to replace the faulty ones and issues a fault warning.
9. A vehicle stability control system, characterized in that: The vehicle stability control method based on multi-sensor fusion as described in any one of claims 1 to 8, wherein the control system comprises: The multi-sensor perception fusion module is used to acquire information from multiple sensors and calculate the road surface adhesion coefficient. The road surface adhesion coefficient segment prompt module is used to receive the data output by the multi-sensor perception fusion module, calculate the average coefficient and reliable quantitative value of each road segment, and send the distance to the vehicle range, adhesion level and reliable quantitative value of each road segment to the vehicle instrument for display. The warning module is used to monitor the average coefficients. When there is at least one low-level road segment with an average coefficient not greater than the preset warning value among multiple road segments, the vehicle instrument will issue a warning. The vehicle stability control module is used to control the master cylinder to build up pre-braking pressure and control the drive system to reduce output torque; The functional safety protection module is used to monitor multi-sensor signals in real time. When a sensor failure is detected, it controls the redundant sensor to replace it and issues a fault prompt.
10. A vehicle, characterized in that: Includes the vehicle stability control system as described in claim 9.