A vehicle all-terrain adaptive control method and system and vehicle

Abstract

The application discloses a vehicle all-terrain adaptive control method and system and a vehicle, and belongs to the technical field of intelligent automobiles. In view of the single triggering, rough strategy and lack of cooperation of the prior art, the application identifies a front target through multi-sensor fusion, and parallel computing geometry passability, harmfulness, avoidable index and braking distance margin. A double-matrix matching coping mode is driven by the geometric passability, a cooperative coping strategy package containing a suspension damping, a driving torque, a steering auxiliary torque and a human-computer interaction instruction sequence is generated based on the coping mode and the target type combination, and is sequentially issued to each subsystem for cooperative execution. The application is used for active safety and chassis cooperative control of the intelligent automobile, realizes diversified road condition fine grading response, and improves the human-vehicle cooperation efficiency and driving safety.

Description

Technical Field

[0001] This application relates to the field of intelligent vehicle technology, and in particular to a vehicle adaptive control method, system and vehicle for all road conditions. Background Technology

[0002] In the field of vehicle active safety and chassis control, one type of existing technology scans the road surface ahead using cameras or millimeter-wave radar installed at the front of the vehicle to obtain shape information of targets such as potholes, speed bumps, and road protrusions, and adjusts the active suspension damping in advance accordingly to mitigate the impact of road bumps on the vehicle body. Another type uses radar or visual sensors to sense the distance and relative speed of targets ahead, triggering braking or issuing a warning to the driver when a collision risk is detected. However, the above-mentioned existing technologies typically use the longitudinal distance between the target and the vehicle or the collision time margin as a single trigger condition, applying a uniform response logic to targets of different types and physical sizes. In real-world road scenarios with diverse target characteristics, this single trigger mechanism is difficult to match with actual safety requirements. Summary of the Invention

[0003] Embodiments of this application provide a vehicle all-road-condition adaptive control method and system to cope with targets ahead of it of different types and physical sizes.

[0004] To achieve the above objectives, a first aspect of the present invention provides a vehicle all-road-condition adaptive control method, comprising the following steps: during vehicle operation, receiving multi-sensor data in real time and performing target identification; in response to the target identification result, acquiring target attribute information, surrounding vehicle environment parameters, and vehicle state parameters of the target; determining the geometric passability, hazard level, avoidance index, and braking distance margin of the target based on the target attribute information and vehicle state parameters; matching a response mode based on the geometric passability, the hazard level, the avoidance index, and the braking distance margin; generating a collaborative response strategy package from a preset control strategy mapping relationship based on the combination of the response mode and the target type of the target; and distributing the collaborative response strategy package to each subsystem for execution.

[0005] Furthermore, the geometric passability of the target is determined based on the target's type, physical properties, and the vehicle's chassis parameters.

[0006] Furthermore, based on the type and physical properties of the target, the hazard level of the target is determined.

[0007] Specifically, based on the distance of the target relative to the vehicle, the surrounding vehicle environment parameters, and the vehicle's state parameters, an avoidability index for the target is determined.

[0008] Further, determining the avoidable index for the target includes: calculating the left-hand avoidable index and the right-hand avoidable index respectively, and taking the larger value of the two as the output avoidable index; at the same time, determining the direction corresponding to the larger value as the optimal avoidable direction, and calculating the lateral space-time margin, longitudinal braking margin and vehicle attitude stability for the optimal avoidable direction.

[0009] Furthermore, the matching response mode includes: when the geometric passability is passable, determining the corresponding conventional mode based on the avoidability index and the hazard level; when the geometric passability is impassable, determining the corresponding emergency mode based on the avoidability index and the braking distance margin.

[0010] Furthermore, the collaborative response strategy package includes at least one of the following: suspension damping target curve, drive torque compensation sequence, steering assist torque sequence, and human-machine interaction command sequence.

[0011] Furthermore, the shape of the suspension damping target curve is determined according to the type of the target, and the amplitude of the suspension damping target curve is determined according to the vehicle body attitude stability.

[0012] Furthermore, the guiding amplitude of the steering assist torque in the steering assist torque sequence is jointly determined by the maximum rated torque of the electric power steering system, the lateral space-time margin, and the avoidance index.

[0013] Furthermore, the deceleration amplitude in the drive torque compensation sequence is determined based on the longitudinal braking margin.

[0014] The present invention also provides a vehicle all-road-condition adaptive control system, comprising: a target recognition module configured to receive multi-sensor data in real time during vehicle operation and perform target recognition; a data acquisition module configured to acquire target attribute information, surrounding vehicle environment parameters, and vehicle state parameters in response to the target recognition result; a comprehensive evaluation module configured to determine the geometric passability, hazard level, avoidance index, and braking distance margin of the target based on the target attribute information and vehicle state parameters; a response decision module configured to match a response mode based on the geometric passability, the hazard level, the avoidance index, and the braking distance margin; a strategy generation module configured to generate a collaborative response strategy package from a preset control strategy mapping relationship based on the combination of the response mode and the target type of the target; and a strategy distribution module configured to distribute the collaborative response strategy package to each subsystem for execution.

[0015] The present invention also provides a vehicle, comprising: a memory for storing a computer program; and a processor electrically connected to the memory for executing the computer program to implement the above-described vehicle all-road-condition adaptive control method.

[0016] The above one or more technical solutions, by introducing geometrical accessibility as a prerequisite, avoid the problem of ineffective avoidance calculations for impassable targets in existing technologies, significantly improving decision-making efficiency and safety. At the same time, by combining the avoidability index and hazard level to determine the response strategy, a refined graded response to diverse road conditions is achieved. Attached Figure Description

[0017] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0018] Figure 1 A schematic diagram of the hardware architecture of the vehicle all-road condition intelligent early warning and adaptive control system provided in the embodiment; Figure 2 A flowchart illustrating the vehicle all-road-condition adaptive control method provided in this embodiment; Figure 3 This is a schematic diagram of the module architecture of the vehicle all-road-condition adaptive control system provided in the embodiment. Detailed Implementation

[0019] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application. In the description of the embodiments of this application, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] As described in the background section, existing technologies typically use the longitudinal distance between the target and the vehicle or the collision time margin as a single triggering condition to deal with targets in front. They employ a uniform response logic for targets of different types and physical sizes, which does not meet the needs of dealing with diverse targets in complex road conditions.

[0021] To address the aforementioned issues, this application provides a vehicle all-road-condition adaptive control method. This scheme uses multi-sensor fusion perception as input, firstly identifying targets ahead in real time; then matching response modes to targets based on geometric passability and hazard level, and further matching cooperative control strategy packages based on these response modes. This achieves differentiated response strategies for targets with diverse characteristics, avoiding ineffective avoidance and improving response efficiency.

[0022] Figure 1 A schematic diagram of an example environment 100 in which various embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, the example environment includes a vehicle equipped with a multimodal perception unit 110, a central fusion decision-making unit 120, a chassis collaborative execution unit 130, and a human-machine interaction unit 140.

[0023] The multimodal perception unit 110 is responsible for collecting information about the vehicle's external environment and its own state, including a forward environment perception module 111 and a vehicle dynamics perception module 112. The forward environment perception module 111 integrates a forward-facing camera, millimeter-wave radar, and optional lidar, and is installed at the front of the vehicle. It ensures spatiotemporal alignment of multimodal data through hard synchronization with timestamps. The vehicle dynamics perception module 112 includes wheel speed sensors installed on all four wheels, a six-axis inertial measurement unit located near the vehicle's center of gravity, and suspension travel sensors integrated into the active dampers.

[0024] The central fusion decision unit 120 can be an automotive-grade domain controller equipped with a multi-core processor and an artificial intelligence acceleration module. It is electrically connected to the multimodal perception unit 110, the chassis collaborative execution unit 130, and the human-machine interaction unit 140 via a high-speed automotive Ethernet or CAN FD bus, and serves as the core computing and decision-making hub of the system. In some embodiments, this unit can also be deployed on an in-vehicle edge computing node or a cloud collaborative server.

[0025] The chassis cooperative execution unit 130 includes an adjustable damping suspension system 131, a power and braking control system 132, and a steering assistance system 133. The adjustable damping suspension system 131 consists of four independent continuously damped controlled shock absorbers and their controllers. The power and braking control system 132 integrates the vehicle controller, motor controller, and electronic stability program control module. The steering assistance system 133 is an electric power steering system with steer-by-wire or strong assistance capabilities.

[0026] The human-machine interface unit 140 includes a display 141, a full LCD instrument panel 142, and a multi-channel vehicle audio system 143. The display 141 is preferably an augmented reality head-up display (HUD), capable of projecting virtual guidance graphics with a specific depth of field onto the real road outside the driver's windshield, achieving visual fusion display. In some embodiments, the display 141 may also be a conventional head-up display, a central control screen, or other display devices commonly used in the art, as long as they can output tiered, contextualized guidance information to the driver.

[0027] The output of the multimodal perception unit 110 is connected to the data interface of the central fusion decision unit 120, continuously transmitting the perception data stream. The control command output of the central fusion decision unit 120 is connected to the control interfaces of each subsystem of the chassis collaborative execution unit 130 and the signal input interface of the human-machine interaction unit 140, respectively. In this embodiment, data acquisition and processing strictly comply with relevant laws and regulations and are used only for vehicle all-road-condition adaptive control, not for any other unauthorized purposes. It should be understood that the above example environment is described for illustrative purposes only and does not imply any limitation on the scope of protection of this application. This application can also be applied to other scenarios such as intelligent connected vehicles and autonomous driving test platforms, and the environment may also include components such as sensors and communication modules not shown.

[0028] Figure 2 An example flowchart of a vehicle all-road-condition adaptive control method 200 according to some embodiments of this application is shown. The example method can be, for example, from... Figure 1 The central fusion decision-making unit 120 shown executes this. It should be understood that the method may also include additional actions not shown, and the scope of this application is not limited in this respect. The following is in conjunction with... Figure 1 and Figure 2 Each step is described in detail.

[0029] In box 210, during vehicle operation, multi-sensor data is received in real time, and target recognition is performed. For example, the central fusion decision unit 120 can continuously receive multiple data streams from the multimodal perception unit 110 and perform real-time recognition of targets on the road ahead. Specifically, the forward-looking camera in the forward environment perception module 111 can continuously acquire RGB images of the road ahead and input them into a trained deep convolutional neural network for real-time semantic segmentation and target detection. This network can output pixel-level type labels for targets ahead, such as "potholes," "speed bumps," "road protrusions," "scattered objects," "water accumulation areas," and "smooth road surfaces," as well as the corresponding two-dimensional bounding boxes for each target.

[0030] For example, the deep convolutional neural network can adopt an encoder-decoder structure. The encoder extracts multi-scale features of the image through multi-layer convolution and pooling operations, and the decoder fuses high-level semantic features with low-level detail features through upsampling and skip connections. Finally, it outputs the type label of the road target ahead at the pixel level, such as "pothole", "speed bump", "road protrusion", "scattered objects", "water accumulation area", "smooth road surface", etc., and generates the corresponding two-dimensional bounding box for each target. In some embodiments, the network can also output the confidence score of each target category. When the confidence score is lower than a preset threshold, the corresponding target can be marked as a state to be verified, and cross-confirmed by subsequent millimeter-wave radar data to reduce the impact of false detections on downstream decisions. It should be understood that the specific architecture of the deep convolutional neural network is not limited. Those skilled in the art can choose a lightweight network (e.g., a compressed model suitable for edge inference) or a precision-first network architecture according to the computing power constraints and real-time requirements of the vehicle computing platform, as long as it can output the target type label and the corresponding two-dimensional bounding box.

[0031] Meanwhile, the millimeter-wave radar in the forward-facing environmental perception module 111 continuously provides longitudinal distance, radial velocity, azimuth angle, and point cloud information of the target ahead. The central fusion decision unit 120 can correlate and match the target's two-dimensional image position identified by the camera with the point cloud detected by the millimeter-wave radar in the same spatial area. Using the density and reflection intensity information of the radar point cloud, it can more accurately estimate the target's three-dimensional feature parameters, such as the depth h and width w of pits, and the vertical height d of debris. It should be understood that the camera and millimeter-wave radar are co-located in physical space, and hard synchronization via timestamps ensures that the two data streams are aligned in time and space to guarantee the accuracy of the correlation and matching.

[0032] In some embodiments, the forward environment perception module 111 may further include a lidar, which is used to provide three-dimensional point cloud data as supplementary verification in complex weather conditions or scenarios where the accuracy of the target's three-dimensional contour is required, thereby further improving the estimation accuracy of the target feature parameters.

[0033] The vehicle dynamic perception module 112 can simultaneously collect data from wheel speed sensors installed on the four wheels, data from a six-axis inertial measurement unit located near the vehicle's center of gravity, and data from suspension travel sensors integrated into the active dampers, in order to calculate the vehicle's current speed in real time. Road surface adhesion coefficient The estimated values ​​and suspension travel margin provide the vehicle's status input for subsequent parameter acquisition and evaluation calculations.

[0034] In box 220, in response to the target recognition result, the target attribute information, surrounding vehicle environment parameters, and vehicle state parameters of the target are acquired. For example, the central fusion decision unit 120 can extract target attribute information in response to the target recognition output in box 210. The target attribute information includes: target type labels, such as "pothole", "speed bump", "scattered objects", "water accumulation area", etc.; physical attribute parameters of the target, such as characteristic dimensions such as pothole depth h, width w, and vertical height d of scattered objects; longitudinal distance and radial relative velocity of the target relative to the vehicle, and lateral offset of the target center relative to the centerline of the vehicle. .

[0035] It should be understood that the accuracy of the estimation of the aforementioned target physical attribute parameters directly affects the accuracy of subsequent geometric accessibility judgment and hazard level assessment. In some embodiments, for targets with low confidence in the fusion estimation results of camera and millimeter-wave radar (e.g., sparse radar point cloud due to obstruction or small target size), the central fusion decision unit 120 can mark the target's physical attribute parameters as low confidence and automatically use conservative calibration values ​​in subsequent hazard level assessments to ensure that the decision results are biased towards the safety side.

[0036] In some embodiments, the vehicle state parameters can be calculated from the real-time data of the vehicle dynamics perception module 112. Specifically, the current vehicle speed... The ground clearance of the chassis can be calculated by averaging the data from the wheel speed sensors of all four wheels. The front axle track width can be dynamically estimated by real-time readings from the suspension travel sensor integrated within the active damper, combined with pre-stored chassis geometry parameters (the correspondence between the calibrated ground clearance and suspension travel under full load). Pre-stored vehicle chassis parameters are read directly; road surface adhesion coefficient. The longitudinal acceleration data can be jointly estimated by wheel speed sensor data and six-axis inertial measurement unit data. For example, the degree of tire slippage can be identified by comparing the difference between the drive wheel angular acceleration and the vehicle body longitudinal acceleration, and then the road adhesion coefficient can be calculated. The suspension travel margin can be calculated by the difference between the current suspension travel sensor reading and the maximum suspension compression travel, and is used for subsequent mandatory upgrade correction judgment of hazard level.

[0037] In some embodiments, the estimation of the road surface adhesion coefficient μ can also be combined with the yaw rate and lateral acceleration data output by the vehicle's electronic stability program control module, and extended Kalman filtering or other state estimation methods can be used for more accurate online identification to improve the estimation accuracy in low-adhesion road surface scenarios (such as wet and slippery roads, icy and snowy roads).

[0038] In some embodiments, the surrounding environment parameters can be calculated from the perception results of the traffic flow in adjacent lanes by the forward environment perception module 111. Specifically, the central fusion decision unit 120 can calculate the collision time margin for the left adjacent lane based on the distance and relative speed measurements of the nearest preceding or following vehicle in the left adjacent lane and the right adjacent lane by millimeter-wave radar. Collision time margin with the right adjacent lane .For example, It can be calculated by dividing the longitudinal distance between the target and the vehicles in the adjacent lane by their relative speeds. Calculated using the same logic. and The time margin for collision with vehicles in adjacent lanes when laterally avoiding a collision to the left or right is quantified. The larger the value, the higher the feasibility of lateral avoidance space in the corresponding direction.

[0039] In some embodiments, if the millimeter-wave radar does not detect a valid target in an adjacent lane on one side, that is, there is currently no vehicle in that lane, the TTC value in the corresponding direction can be set to a preset maximum safety threshold, indicating that there is sufficient lateral avoidance space in that direction.

[0040] In box 230, based on the target attribute information and the vehicle's state parameters, the target's geometric passability, hazard level, avoidance index, and braking distance margin are determined.

[0041] Since different types of targets pose different threats to the chassis, before entering the response mode, we first determine whether the vehicle can directly run over the target without causing a chassis collision based on the geometric relationship between the target's physical dimensions and the vehicle's chassis parameters.

[0042] For example, the central integrated decision-making unit 120 can select the corresponding judgment logic based on the target type label. For potholes and waterlogged areas, since they belong to the road surface deformation category, the wheels can directly sink into or pass through them, and there is no risk of chassis collision. The geometric passability can be directly determined as "passable" without performing parameter comparison, thus reducing unnecessary computational overhead. For speed bumps, as road speed limit facilities, their design height is based on the premise that vehicles can pass safely, and there is no risk of chassis collision. Similarly, the geometric passability can be directly determined as "passable" without performing parameter comparison.

[0043] For debris, since its shape, size and distribution on the road surface are not constrained by road design specifications, the threat may come from two independent dimensions, namely the lateral and vertical dimensions. It needs to be judged separately. It can only be judged as "passable" when both dimensions meet the passability conditions.

[0044] For example, the judgment logic for the lateral dimension is as follows: compare the relationship between the lateral width of the debris and the front axle track. If the lateral width of the debris is less than the front axle track, the debris is located between the two wheels, and the vehicle can directly bypass the wheel tracks, thus the lateral dimension is judged as "passable". If the lateral width of the debris is not less than the front axle track, the vehicle cannot bypass the wheel tracks, and regardless of the vertical height, it is directly judged as "impassable", without the need for vertical comparison. The judgment logic for the vertical dimension is as follows: compare the relationship between the vertical height of the debris and the chassis ground clearance. If the vertical height of the debris is less than the chassis ground clearance, the vehicle chassis can directly pass through without a collision, thus the vertical dimension is judged as "passable"; otherwise, it is judged as "impassable". It should be understood that the judgments of the above two dimensions are independent of each other. Only when both the lateral and vertical dimensions are judged as "passable" is the geometric passability of the debris ultimately judged as "passable"; if either dimension is judged as "impassable", the entire object is judged as "impassable".

[0045] Therefore, by pre-judging geometric passability, invalid conventional avoidance calculations are avoided for impassable targets, significantly improving decision-making efficiency and safety.

[0046] For geometrically passable targets, although they will not cause chassis collisions, running over or passing through them still poses varying degrees of impact threat to the vehicle chassis and occupants. The hazard level quantifies this potential threat into three levels: low, medium, and high. This allows the system to work with the avoidability index in subsequent conventional sub-matrixes to drive the selection of response modes, ensuring that the system takes appropriate measures to respond to targets with different levels of hazard.

[0047] For example, in the offline phase, the peak vertical acceleration of the vehicle body and the suspension travel compression can be used as evaluation indicators. Through simulation or real vehicle testing, a three-dimensional indexed damage potential comparison table can be established according to three dimensions: target type, characteristic parameter range, and vehicle speed range, and the hazard level can be marked as low, medium, and high.

[0048] In some embodiments, when the suspension travel margin falls below a preset threshold, a forced escalation correction can be triggered, raising the hazard level by one grade. This is because insufficient suspension travel margin means a decrease in the shock absorber's damping capacity; under these conditions, the actual impact of the same target on the occupant will be higher than the calibrated value under normal conditions. The forced escalation correction can reflect this additional threat, ensuring that the decision-making outcome is biased towards the safety side. In some embodiments, for debris, since its material hardness parameter is difficult to obtain in real time through online sensors, a preset conservative calibration value can be used instead of the hardness input, causing the hazard level assessment result to be biased towards a higher level to cover the worst-case scenario.

[0049] Therefore, by combining offline calibration with a conservative correction mechanism, the assessment is made real-time while ensuring that the hazard level always leans towards the safety side in scenarios where data is incomplete or the chassis condition is poor.

[0050] The feasibility of lateral avoidance is constrained by three factors: the lateral position of the target relative to the vehicle, the relative motion between the vehicle and the target, and the temporal and spatial margins of adjacent lanes. Furthermore, the feasibility of avoidance on the left and right sides is usually asymmetrical; calculating each independently and taking the larger value can automatically identify the optimal avoidance direction, providing a clear basis for the direction and magnitude of the steering assist torque in subsequent strategy packages. For example, the central fusion decision unit 120 can base its decision on the target's lateral offset. Target relative velocity and adjacent lane collision time margin and Calculate the leftward avoidability index independently. Rightward avoidance index The leftward avoidance index Taking the calculation as an example: the target lateral offset... Estimate the minimum lateral displacement required to avoid the target to the left by summing it to half the target's lateral width; this is the minimum distance the vehicle's centerline needs to shift to the left. Then, divide this displacement by the current vehicle speed. Estimate the minimum time required to complete the avoidance by comparing the ratio; then compare this time with... Comparison, The larger the difference between the actual time and the required time, the better. The higher the value, the lower the value, ultimately normalizing the result to a continuous interval between 0 and 1. Rightward Avoidability Index Based on the same logic Calculate. and The larger value in the formula is used as the output avoidability index, and the corresponding direction is determined as the optimal avoidance direction. It should be understood that the above calculation method is only an exemplary implementation. Other calculation methods that can comprehensively reflect the lateral displacement requirements and the time and space margin of adjacent lanes can also be used, as long as the output value is a continuous value in the range of 0 to 1 and can distinguish between left and right directions.

[0051] For the optimal avoidance direction, three risk decomposition indices can be further calculated to quantify the risk levels in the lateral, longitudinal, and attitude dimensions, providing independent inputs for the quantitative calculation of various control parameters in the subsequent strategy package. Lateral spatiotemporal margin This reflects the urgency of a collision with a vehicle in the adjacent lane in the current optimal avoidance direction, and can be expressed as the optimal direction collision time margin. With preset safety threshold The ratio is calculated to obtain, A larger value indicates a tighter lateral avoidance space, and the subsequent steering assist torque should be reduced accordingly to avoid forcibly pushing into the danger zone. Longitudinal braking margin. The braking safety margin reflecting the current vehicle speed and target distance can be expressed as the target collision time margin. With longitudinal security threshold The ratio is calculated to obtain, A larger value indicates a tighter longitudinal braking space, requiring a correspondingly larger deceleration amplitude from the subsequent drive torque. Vehicle body posture stability. The risk of vehicle roll and instability when performing a lateral avoidance steering maneuver at the current speed can be expressed by the formula... Calculation, where Current vehicle speed It is the acceleration due to gravity. Minimum turning radius, The road surface adhesion coefficient; A larger value indicates a higher risk of roll instability, and the subsequent suspension damping amplitude should be increased accordingly to prioritize vehicle stability.

[0052] For geometrically impassable targets, the sufficiency of the longitudinal braking space determines the choice of emergency response mode. Sufficient braking distance allows for mandatory lateral guidance, while insufficient braking distance necessitates emergency braking or even degraded handover due to exceeding limits. Therefore, for geometrically impassable targets, braking distance margin is also calculated. For example, the central fusion decision unit 120 can base its decision on the current vehicle speed. Calculate the shortest braking distance of the vehicle at the current speed based on the preset maximum deceleration parameters. Then Current longitudinal distance from the target A ratio is calculated to determine the braking distance margin. A smaller ratio indicates more braking space, while a larger ratio (approaching or exceeding 1) indicates that full braking is difficult to achieve within the current distance. In some embodiments, the shortest braking distance... The calculation can be combined with the current road surface adhesion coefficient. The adjustments are made to reflect the actual increase in braking distance under low adhesion conditions such as wet and slippery roads, so that the assessment results of braking distance margin can accurately reflect the true braking capacity under different road conditions.

[0053] In box 240, a response mode is determined based on the geometric passability, the hazard level, the avoidability index, and the braking distance margin.

[0054] It is understandable that for geometrically passable targets, the goal is to select the optimal passage or avoidance method while ensuring comfort; for geometrically impassable targets, the goal is to ensure safety within limited time and space. Therefore, by pre-diverting traffic based on geometrical passability, ineffective avoidance calculations for impassable targets are avoided, making the response plan more consistent with actual road conditions and improving decision-making efficiency and safety.

[0055] For geometrically passable targets, the choice of response strategy needs to consider both the potential threat level of the target to the vehicle and the current feasibility of lateral avoidance. Decision-making based solely on hazard level ignores the feasibility of lateral avoidance, leading to the generation of guidance avoidance instructions even when avoidance space is insufficient; decision-making based solely on avoidability index ignores the actual threat level of the target, resulting in insufficient response to high-hazard targets. Therefore, constructing a first sub-matrix using avoidability index and hazard level as indices, and covering diverse common road condition scenarios through combinations of these two dimensions, allows each scenario combination to correspond to a suitable response mode.

[0056] For example, the central integrated decision-making unit 120 can divide the avoidability index into three ranges: high, medium, and low, and the hazard level into three levels: low, medium, and high. It can then use the combination of these two as an index to query the corresponding response mode in the first submatrix. Table 1 shows a non-restrictive example of the response modes corresponding to the combination of avoidability index and hazard level in the first submatrix.

[0057] Table 1 Example of the first submatrix response mode mapping

[0058] In some embodiments, the thresholds for dividing the high, medium, and low avoidability index ranges can be determined based on statistical data from actual road scenarios. For example, an avoidability index above 0.7 is considered a high range, 0.3 to 0.7 is considered a medium range, and below 0.3 is considered a low range. The above thresholds do not constitute a limitation on the scope of protection of this application, and those skilled in the art can adjust them according to actual needs.

[0059] Taking the active guidance avoidance mode as an example, when the avoidability index is high and the hazard level is low, the target threat level is low and there is ample lateral avoidance space. The system can generate a strategy package that guides the driver to slightly steer in the optimal direction to bypass the target, and the suspension damping is simultaneously pre-adjusted to cope with possible minor bumps. Taking the deceleration hard pass mode as an example, when the avoidability index is low and the hazard level is medium, the target threat level is moderate but the lateral avoidance space is insufficient. The system can generate a strategy package that focuses on deceleration to reduce impact without applying lateral guidance, and the suspension damping is simultaneously adjusted to a stiffer state to absorb the impact to the maximum extent. Taking the enhanced deceleration hard pass mode as an example, when the avoidability index is low and the hazard level is high, the system can further increase the deceleration magnitude based on the deceleration hard pass mode and issue a higher level of warning prompts to the driver through the human-machine interaction unit 140.

[0060] For geometrically impassable targets, the hazard level is no longer the main driving parameter for decision-making, because the impassable target itself constitutes a threat that must be avoided, and the response mode depends on lateral evasiveness and vertical braking space.

[0061] For example, the central integrated decision-making unit 120 can divide the avoidability index into two ranges: high and low-medium, and the braking distance margin into two ranges: sufficient and insufficient. It can then use the combination of these two as an index to query the corresponding response mode in the second submatrix. Table 2 shows a non-restrictive example of the response modes corresponding to the combination of avoidability index and braking distance margin in the second submatrix.

[0062] Table 2 Examples of Second Submatrix Response Pattern Mapping

[0063] In some embodiments, the threshold for distinguishing between sufficient and insufficient braking distance margin can be set as follows: when the braking distance margin is less than a preset safety threshold (e.g., 0.8), it is determined to be sufficient; when it is greater than or equal to the threshold, it is determined to be insufficient. The above threshold does not constitute a limitation on the scope of protection of this application.

[0064] Taking the forced guided avoidance mode as an example, when the avoidance index is high and the braking distance is sufficient, the target is impassable but the lateral avoidance space is feasible and the longitudinal braking space is ample. The system can generate a strategy package that primarily uses forced lateral guidance while simultaneously applying moderate deceleration. The steering assist system 133 applies a large guiding torque to ensure the vehicle completes lateral maneuvering. Taking the forced guided avoidance plus simultaneous braking mode as an example, when the avoidance index is high but the braking distance is insufficient, the system superimposes an emergency braking command while performing forced lateral guidance to simultaneously complete deceleration and lateral maneuvering within a limited distance. Taking the emergency braking mode as an example, when the avoidance index is medium or low and the braking distance is sufficient, the lateral avoidance space is insufficient. The system generates a strategy package that primarily uses emergency braking with maximum deceleration, without applying lateral guidance to avoid the risk of a side collision when the avoidance space is insufficient. Taking the over-limit downgrade handover mode as an example, when the avoidance index is medium or low and the braking distance is insufficient, the system has exceeded the boundary of automatic processing capability. The central fusion decision unit 120 can immediately transfer control to the driver and issue the highest level warning through the human-machine interaction unit 140, while still performing maximum braking deceleration to reduce the collision speed as much as possible.

[0065] Therefore, the system can precisely match appropriate response modes to real road scenarios with diverse target characteristics, overcoming the technical shortcomings of existing technologies where a single triggering mechanism cannot accurately match actual safety needs and response strategies are too crude.

[0066] In box 250, a collaborative response strategy package is generated from a preset control strategy mapping relationship based on the combination of the response mode and the target type of the target. For example, the central fusion decision unit 120 can use the combination of the response mode and target type label output in box 240 as an index to retrieve and generate a structured collaborative response strategy package from the preset control strategy mapping relationship. This strategy package includes a suspension damping target curve, a drive torque compensation sequence, a steering assist torque sequence, and a human-machine interaction command sequence, which correspond to the control inputs of the four subsystems: the adjustable damping suspension system 131, the power and braking control system 132, the steering assist system 133, and the human-machine interaction unit 140, respectively.

[0067] In some embodiments, the excitation experienced by the wheels differs when a vehicle passes over different types of road surfaces. For example, potholes generate a single impact, speed bumps generate continuous bidirectional bulges, debris generates irregular disturbances in multiple directions, and water accumulation generates low-frequency continuous resistance. A single-mode damping curve cannot simultaneously accommodate these different excitation modes; therefore, the temporal shape of the damping curve should be determined by the target type.

[0068] For example, the central fusion decision unit 120 can determine the shape of the suspension damping target curve based on the target type label: when the target type is a pothole, a single-pulse curve is used, with a smaller damping value during the wheel sinking stage to fully absorb the impact energy, and a larger damping value during the rebound stage to suppress the bounce; when the target type is a speed bump, a double-peak symmetrical curve is used, with a larger damping value during the wheel climbing stage to resist the bulge impact, and a smaller damping value during the landing stage to buffer the landing vibration; when the target type is scattered debris, a multi-directional disturbance curve is used, maintaining a larger damping value throughout to cope with irregular impacts from multiple directions; when the target type is a water accumulation area, a low-frequency continuous curve is used, maintaining a medium damping value throughout to cope with the low-frequency vehicle pitch caused by road resistance.

[0069] The amplitude of the suspension damping target curve can be determined by the vehicle body attitude stability. Quantitative drive. For example, target damping value. The following formula can be used for calculation:

[0070] in and These are the minimum and maximum damping values ​​of the continuously damped control shock absorber in the adjustable damping suspension system 131, respectively. The larger the value, the higher the risk of roll instability. The closer Prioritize vehicle stability; A smaller value indicates a lower risk of roll. The closer It also takes into account passenger comfort.

[0071] At the same time, the magnitude of the damping curve determines the ability to suppress body roll. It should be quantitatively driven by the degree of roll instability risk during the current lateral avoidance steering, rather than a fixed setting, in order to achieve a dynamic balance between comfort and stability.

[0072] In some embodiments, the suspension damping target curve can adopt a three-segment timing structure to achieve a predictive response to the target rather than a passive response. The pre-adjustment segment is triggered before the target reaches the wheels, and the triggering time is determined by the target's current longitudinal distance and vehicle speed. The damping value is determined in real time and transitions linearly from the current operating value to the target value. To avoid sudden damping changes that could cause vehicle body impact; to maintain the target damping value during the period from wheel contact with the target to complete passing of the target. This ensures consistent damping throughout the entire process; the recovery phase is triggered after the vehicle has completely passed the target, and the damping value smoothly returns to the normal working value with a preset slope, avoiding sudden changes that could cause vehicle body sway.

[0073] In some embodiments, in response modes including lateral guidance (e.g., active guidance avoidance mode, forced guidance avoidance mode), the four independent continuously damping controlled shock absorbers of the adjustable damping suspension system 131 can apply differentiated damping: the damping value of the shock absorber biased towards the target side is greater than the damping value of the shock absorber away from the target side, so as to suppress body roll during lateral avoidance steering through left-right asymmetrical damping distribution and improve body stability during lateral maneuvering.

[0074] Therefore, by driving the damping curve shape by target type, quantitatively driving the damping amplitude by vehicle posture stability, and achieving predictive response through a three-segment time sequence structure, the suspension damping control can simultaneously possess the ability to specifically match road excitation characteristics and the ability to dynamically ensure lateral maneuver stability.

[0075] The magnitude of longitudinal deceleration control should be related to the tightness of the current longitudinal braking space. The tighter the braking space, the greater the deceleration should be to reduce the collision speed as quickly as possible. When the braking space is ample, the deceleration can be appropriately reduced to avoid unnecessary abrupt deceleration affecting ride comfort. Furthermore, in lateral avoidance response modes, the demand for road grip during lateral maneuvers competes with longitudinal braking. If the longitudinal deceleration is too large, it may deplete the tire grip margin, leading to lateral avoidance failure. Therefore, in response modes that include lateral guidance, the drive torque compensation sequence needs to add an additional grip margin compensation on top of the longitudinal deceleration to reserve sufficient grip margin for lateral maneuvers.

[0076] For example, the deceleration magnitude of the drive torque compensation sequence can be determined by the longitudinal braking margin. Quantitative driving: The larger the value, the tighter the longitudinal braking space, and the greater the corresponding deceleration amplitude; A smaller value indicates more braking space and a correspondingly smaller deceleration amplitude. The deceleration process can be generated in the form of a trapezoidal deceleration curve, including an acceleration and deceleration segment (the deceleration amplitude increases linearly to the target value), a holding segment (maintaining the target deceleration amplitude), and a recovery segment (the deceleration amplitude recovers linearly), in order to avoid sudden changes in deceleration causing a shock to the occupants.

[0077] In some embodiments, in a response mode including lateral guidance, the drive torque compensation sequence can be modified based on the aforementioned basic deceleration amount, according to the lateral adhesion required for lateral avoidance, to reserve sufficient adhesion margin for lateral maneuvering. Correction value The calculation can be based on the current road surface adhesion coefficient. The minimum lateral adhesion force required for lateral avoidance is estimated to ensure that the combined longitudinal and lateral forces do not exceed the maximum available adhesion force of the current road surface. It should be understood that the above superposition method uses an intervention approach that does not override the driver's active throttle requests; that is, deceleration compensation is applied only if the driver does not actively accelerate, in order to avoid conflict with driver operation.

[0078] Therefore, by quantitatively driving the deceleration amplitude through longitudinal braking margin, the deceleration control is precisely matched with the actual braking space tension; by superimposing the adhesion margin compensation, sufficient margin is reserved for the lateral adhesion of the tires in lateral avoidance scenarios to ensure the effectiveness of lateral maneuvering.

[0079] The steering assist torque guidance range needs to strike a balance between three constraints: First, the guidance torque should not exceed the rated torque limit of the electric power steering system to ensure a safety boundary; second, the guidance torque range should be positively correlated with the current lateral avoidance feasibility (avoidance index), with higher avoidance feasibility resulting in more aggressive guidance; third, when the adjacent lane space is relatively tight (lateral space margin). (If the value is relatively large), the guiding torque should converge accordingly to avoid forcibly pushing the driver into the danger zone. Coupled with the above three constraints in a product form, the triple requirements of safety boundary constraints, feasibility-driven mechanisms, and space constraint correction can be met simultaneously.

[0080] For example, the guiding amplitude of the steering assist torque sequence. The following formula can be used for calculation:

[0081] in This represents the maximum rated torque of the electric power steering system. The avoidability index represents the optimal avoidance direction. This represents the horizontal spatiotemporal margin. When... Approaching 0 (ample lateral clearance) and When it approaches 1 (completely avoidable), Approaching When the guiding torque is at its maximum; Increase (due to limited lateral space) or When the feasibility of avoidance decreases, The intensity decreases accordingly, and the guiding strength automatically converges. The safety limit shall not exceed the preset proportion of the rated torque of the electric power steering system, such as not exceeding 30%, to ensure that the guiding torque is always within the range that the driver can easily overcome, thus guaranteeing the driver's ultimate control over the steering wheel.

[0082] In some embodiments, the guiding torque can be divided into three phases in terms of timing: a guidance setup phase, in which the guiding torque increases linearly from zero to [a specific value] after the response mode is activated. The first stage allows the driver to perceive a clear guiding intention; the second stage, if the vehicle trajectory follows the guiding direction, maintains or moderately increases the guiding torque to encourage the driver to continue following; the third stage, if the steering torque applied by the driver is detected to be opposite to the guiding direction, gradually reduces the guiding torque, transferring control to the driver and avoiding human-machine conflict. It should be understood that the above three-stage timing structure allows the steering assist torque sequence to be dynamically adjusted according to the driver's real-time response behavior, reflecting the driver-led human-machine collaborative design principle.

[0083] Therefore, by coupling the rated torque of the electric power steering system, the lateral space margin, and the avoidance index in a product form to determine the guiding amplitude, and by adapting to different driver response behaviors through a three-stage timing structure, the steering assist torque sequence achieves dynamic matching with the actual avoidance space and the driver's intention while ensuring the safety boundary.

[0084] The situational pressure experienced by drivers varies across different response modes. When the target is passable, the driver has ample time to react, and prompts primarily provide advisory guidance to avoid unnecessary tension. When the target is impassable, time is of the essence, and prompts escalate to warning instructions, clearly informing the driver of the immediate action required. In the over-limit degrading handover mode, the system has exceeded its processing capacity limits, and prompts should be escalated to the highest level warning, explicitly requiring the driver to take over. By strictly binding the levels of human-machine interaction command sequences to the levels of response modes, it is ensured that drivers receive guidance information commensurate with the situational pressure in different contexts, avoiding information overload or insufficient guidance.

[0085] For example, the human-machine interface unit 140 can retrieve and output the corresponding level of instruction sequence from the preset human-machine interface instruction template according to the response mode identifier. In normal guidance modes, such as active guidance avoidance mode and smooth run-over mode, the display 141 can project a green guide line onto the road surface in front of the driver, clearly indicating the suggested driving trajectory. At the same time, the multi-channel in-vehicle audio system 143 can play suggestive voice prompts, such as "It is recommended to slightly adjust the direction to the left to avoid potholes" and "Please hold the steering wheel firmly to pass speed bumps," with a calm tone, focusing on guidance and not causing tension to the driver. In emergency modes, such as forced guidance avoidance mode and emergency braking mode, the warning level is upgraded: the display 141 switches to a red warning guide line, the multi-channel in-vehicle audio system 143 plays warning voices, such as "Turn left immediately" and "Emergency braking," with a rapid tone. At the same time, the full LCD instrument panel 142 displays a bright warning icon, ensuring that the driver perceives the emergency situation in the shortest possible time through multi-channel sensory stimulation. In the over-limit downgrade handover mode, the highest level warning is output, and the voice prompt clearly requires the driver to take over control immediately, such as "Please take over the steering wheel immediately". At the same time, the full LCD instrument panel 142 displays the takeover request icon, and the central fusion decision unit 120 simultaneously executes maximum braking deceleration to reduce the collision speed as much as possible.

[0086] In some embodiments, the specific voice content of the human-computer interaction instruction sequence can be dynamically spliced ​​according to the target type and the optimal avoidance direction. For example, the direction in "suggests turning left to avoid the pothole" is determined by the optimal avoidance direction, and the target type is determined by the target type label. The two are combined to generate context-specific prompts, enabling the driver to obtain complete operation guidance information without shifting their gaze.

[0087] Therefore, by strictly binding the level of the human-computer interaction instruction sequence with the level of the response mode, and dynamically splicing specific prompts according to the target type and the optimal avoidance direction, drivers can obtain appropriate hierarchical and contextualized guidance information under different situational pressures.

[0088] In box 260, the collaborative response strategy package is distributed to each subsystem for execution.

[0089] The four types of control commands in the collaborative response strategy package target different physical subsystems, each with varying response setup times. The adjustable damping suspension system's damping adjustment needs to be pre-adjusted before the wheels reach the target, therefore it should be issued first. The intervention times for drive torque compensation and steering assist torque are next. The output of human-machine interface commands should be synchronized with or slightly earlier than chassis control commands to ensure the driver receives guidance information before the vehicle chassis intervenes, avoiding confusion caused by sudden changes in the vehicle's appearance. Issuing all four types of commands simultaneously without distinguishing their timing may lead to a misalignment between chassis response and driver perception, affecting the effectiveness of human-vehicle collaboration.

[0090] For example, after generating a collaborative response strategy package, the central fusion decision unit 120 can distribute various control commands in the following sequence: First, it sends the suspension damping target curve to the adjustable damping suspension system 131, triggering pre-adjustment damping adjustment so that the shock absorber completes damping transition before the wheel reaches the target; then, it simultaneously sends the drive torque compensation sequence to the power and braking control system 132 and the steering assist torque sequence to the steering assist system 133, triggering deceleration control and the establishment of lateral guiding torque, respectively; at the same time, it sends the human-machine interaction command sequence to the human-machine interaction unit 140, ensuring that the driver receives visual and voice guidance information as soon as the chassis intervenes. It should be understood that the above sequence arrangement can calculate the triggering time of each command in real time based on the current longitudinal distance and vehicle speed of the target, rather than using a fixed delay, to adapt to the actual response needs under different combinations of vehicle speed and target distance.

[0091] Therefore, by using a time-sequential delivery mechanism, the responses of the four subsystems are organically coordinated in time rather than randomly concurrent, ensuring the temporal consistency between chassis control and driver perception, and avoiding subsystem response conflicts or driver perception misalignment caused by simultaneous arrival of commands.

[0092] In some embodiments, the adjustable damping suspension system 131 can receive the suspension damping target curve and then have four independent continuously damping control shock absorbers track and control each other according to their respective target damping value time sequence curves. The shock absorber controller can read the current target damping value at a preset sampling period and drive the solenoid valve to adjust the damping, thereby realizing real-time tracking of the target curve.

[0093] In some embodiments, the power and braking control system 132 can receive a drive torque compensation sequence, and then the vehicle controller can add the torque adjustment amount in the compensation sequence to the current drive torque request in a superimposed manner to achieve the preset deceleration control target without overriding the driver's active throttle request; in the response mode including lateral guidance, the adhesion margin compensation amount Synchronous superposition ensures that the combined longitudinal and lateral forces do not exceed the current maximum available adhesion of the road surface.

[0094] In some embodiments, after receiving the steering assist torque sequence, the steering assist system 133 can apply the assist torque by the electric power steering system in a three-stage sequence of guidance establishment, follow enhancement, and countermeasure attenuation. The electric power steering system controller can read the direction and magnitude of the torque applied by the driver's steering wheel in real time, determine whether the driver is currently in a follow, neutral, or countermeasure state, and accordingly increase the guidance torque appropriately in the follow enhancement stage and gradually decrease the guidance torque in the countermeasure attenuation stage, thereby transferring control to the driver.

[0095] In some embodiments, after receiving a sequence of human-machine interaction commands, the human-machine interaction unit 140 can project guide lines or warning icons onto the display 141 according to the visual content specifications in the command sequence, and the multi-channel vehicle audio system 143 can play the corresponding level of prompt tone according to the voice content specifications in the command sequence. The full LCD instrument panel 142 can simultaneously display the corresponding level of status icons. The three outputs are triggered in parallel, and the driver is ensured to perceive the guidance or warning information in the shortest possible time through multi-channel sensory stimulation of vision and hearing.

[0096] Corresponding to the above embodiments of the vehicle all-road-condition adaptive control method, this application also provides a vehicle all-road-condition adaptive control system 300. This system can be implemented as or included in... Figure 1 In the central integrated decision-making unit 120 shown. For example... Figure 3 As shown, the system may include multiple modules to perform the corresponding actions in the above method embodiments.

[0097] The target recognition module 310 is configured to receive multi-sensor data in real time during vehicle operation, perform the target recognition processing described in block 210 above, and output target type labels and three-dimensional feature parameters.

[0098] The data acquisition module 320 is connected to the target recognition module and is configured to perform the three types of parameter extraction processing described in block 220 above in response to the target recognition result, and output target attribute information, surrounding vehicle environment parameters and vehicle status parameters.

[0099] The comprehensive evaluation module 330, which is signal-connected to the data acquisition module, is configured to perform the four evaluation calculations described in block 230 above, including determining the geometric passability, hazard level, avoidance index, and braking distance margin of the target based on the target attribute information and the vehicle's state parameters.

[0100] The response decision module 340, which is signal-connected to the comprehensive assessment module, is configured to perform the pattern matching described in block 240 above, matching a response mode based on the geometric passability, the hazard level, the avoidability index, and the braking distance margin.

[0101] The strategy generation module 350 is signal-connected to the response decision module and is configured to perform the collaborative response strategy package generation described in block 250 above. Based on the combination of the response mode and the target type of the target, the collaborative response strategy package is generated from a preset control strategy mapping relationship.

[0102] The strategy distribution module 360 ​​is signal-connected to the strategy generation module and is configured to perform the strategy package distribution described in block 260 above, distributing the collaborative response strategy package to each subsystem for execution.

[0103] The functions and methods of each module in the above system correspond one-to-one. It can be deployed in automotive-grade domain controllers in software form, or it can be deployed on vehicle edge computing nodes with corresponding computing capabilities.

[0104] This application also provides a vehicle, which includes a memory and a processor. The memory stores a computer program, and the processor is electrically connected to the memory to execute the computer program to implement the vehicle all-road-condition adaptive control method described in any of the above embodiments. In some embodiments, the processor may be an automotive-grade multi-core processor or a domain controller chip with artificial intelligence acceleration capabilities, and the memory may be an on-board solid-state memory or other non-volatile storage medium.

[0105] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the vehicle all-road-condition adaptive control method described in any of the above embodiments. In some embodiments, the storage medium may be a read-only memory, a random access memory, flash memory, or a solid-state drive, etc.

[0106] This application also provides a computer program product that is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, implement the vehicle all-road-condition adaptive control method described in any of the above embodiments.

[0107] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions that, when loaded and executed on a vehicle domain controller or terminal, generate all or part of the processes or functions described in the embodiments of this application. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic cable, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to the server or terminal, or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, and magnetic tape), an optical medium (e.g., digital video disk (DVD), etc.), or a semiconductor medium (e.g., solid-state drive).

[0108] Furthermore, although the operation steps are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments can also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation can also be implemented individually or in any suitable sub-combination in multiple implementations. It should be understood that the forward environment perception module in the above embodiments is not limited to a combination of a forward-looking camera and millimeter-wave radar. Depending on the actual application requirements, a pure vision scheme, a combination of millimeter-wave radar and lidar, or other multi-sensor fusion schemes commonly used in the art can also be adopted, as long as they can output target type labels and three-dimensional feature parameters. It should be understood that the calculation of the above avoidability index is not limited to a specific implementation based on a cost function model. Other calculation methods that can quantify the feasibility of lateral avoidance can also be used, as long as their output is a continuous value in the range of 0 to 1 and can distinguish between left and right directions. Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely exemplary forms of implementing the claims. The above descriptions are merely exemplary embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A vehicle all-road-condition adaptive control method, characterized in that, Includes the following steps: During vehicle operation, it receives data from multiple sensors in real time and performs target identification. In response to the target recognition result, the target attribute information, surrounding vehicle environment parameters, and vehicle status parameters of the target are obtained; Based on the target attribute information and the vehicle's status parameters, determine the target's geometric passability, hazard level, avoidance index, and braking distance margin. Based on the geometric passability, the hazard level, the avoidability index, and the braking distance margin, a matching response mode is determined; Based on the combination of the response mode and the target type of the target, a collaborative response strategy package is generated from the preset control strategy mapping relationship; The collaborative response strategy package is distributed to each subsystem for execution.

2. The method according to claim 1, characterized in that, The geometric passability of the target is determined based on the target's type, physical properties, and the vehicle's chassis parameters.

3. The method according to claim 1, characterized in that, The hazard level of the target is determined based on its type and physical properties.

4. The method according to claim 1, characterized in that, Based on the distance of the target relative to the vehicle, the surrounding vehicle environment parameters, and the vehicle's state parameters, an avoidability index is determined for the target.

5. The method according to claim 4, characterized in that, Determining the avoidable index for the target includes: calculating the left-hand avoidable index and the right-hand avoidable index respectively, and taking the larger value of the two as the output avoidable index; at the same time, determining the direction corresponding to the larger value as the optimal avoidable direction, and calculating the lateral space-time margin, longitudinal braking margin and vehicle attitude stability for the optimal avoidable direction.

6. The method according to claim 1, characterized in that, The matching response mode includes: when the geometric passability is passable, a corresponding conventional mode is determined based on the avoidability index and the hazard level; when the geometric passability is impassable, a corresponding emergency mode is determined based on the avoidability index and the braking distance margin.

7. The method according to claim 5, characterized in that, The collaborative response strategy package includes at least one of the following: suspension damping target curve, drive torque compensation sequence, steering assist torque sequence, and human-machine interaction command sequence.

8. The method according to claim 7, characterized in that, The shape of the suspension damping target curve is determined according to the type of the target, and the amplitude of the suspension damping target curve is determined according to the vehicle body attitude stability.

9. The method according to claim 7, characterized in that, The guiding amplitude of the steering assist torque in the steering assist torque sequence is determined by the maximum rated torque of the electric power steering system, the lateral space-time margin, and the avoidance index.

10. The method according to claim 7, characterized in that, The deceleration magnitude in the drive torque compensation sequence is determined based on the longitudinal braking margin.

11. A vehicle all-road-condition adaptive control system, characterized in that, include: The target recognition module is configured to receive multi-sensor data in real time and perform target recognition while the vehicle is in motion; The data acquisition module is signal-connected to the target recognition module and is configured to acquire the target attribute information, surrounding vehicle environment parameters and vehicle status parameters of the target in response to the target recognition result. The comprehensive evaluation module, which is signal-connected to the data acquisition module, is configured to determine the geometric passability, hazard level, avoidance index, and braking distance margin of the target based on the target attribute information and the vehicle's state parameters. The response decision module, which is signal-connected to the comprehensive assessment module, is configured to match a response mode based on the geometric passability, the hazard level, the avoidability index, and the braking distance margin. The strategy generation module, which is signal-connected to the response decision module, is configured to generate a collaborative response strategy package from a preset control strategy mapping relationship based on the combination of the response mode and the target type of the target. The strategy distribution module is signal-connected to the strategy generation module and is configured to distribute the collaborative response strategy package to each subsystem for execution.

12. A vehicle, characterized in that, include: Memory, used to store computer programs; A processor, electrically connected to the memory, is configured to execute the computer program to implement the method of any one of claims 1 to 10.

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