Vehicle impact active defense method and vehicle

Abstract

This application discloses a vehicle impact active defense method and vehicle, applied in the field of vehicle active safety technology. The vehicle impact active defense method includes: acquiring the state information of a road object before the vehicle contacts it; determining whether the road object is a threat based on the state information; if determined to be a threat, calculating a set of distinct trigger times based on the inherent response time of the vehicle subsystems, and generating a preventative adjustment command based on the trigger times; and responding to the command by collaboratively adjusting the states of multiple vehicle subsystems before contact. Through proactive perception and prediction, the vehicle's state can be intervened and pre-adjusted before a potential impact occurs, achieving a shift from passively withstanding impacts to actively defending against them, reducing the risk of vehicle loss of control and occupant injury caused by sudden impacts.

Description

Technical Field

[0001] This application relates to the field of vehicle active safety technology, specifically to a vehicle impact active defense method and vehicle. Background Technology

[0002] In the field of active vehicle safety technology, responding to sudden impacts from road obstacles is a crucial aspect of ensuring driving safety. Currently, vehicles primarily rely on passive response mechanisms to cope with road impacts. When wheels slip or become unstable, the vehicle's posture is corrected by adjusting braking force or engine torque. Occupant restraint systems, such as seat belts and airbags, only deploy passively after a collision. These systems all follow a reactive logic, meaning they only activate corresponding response measures after an impact event occurs or the vehicle's condition deteriorates. Due to the lag in response of existing technologies, when a vehicle suddenly encounters a potential threat such as a loose manhole cover, it can only passively absorb the impact, leading to a momentary loss of vehicle posture control and the transfer of impact energy to the occupants, affecting vehicle driving safety and occupant protection effectiveness.

[0003] As intelligent driving technology continues to raise the requirements for driving safety and comfort, how to achieve the transformation from passive response to active defense has become an urgent technical problem to be solved in the field of vehicle active safety technology. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a vehicle impact active defense method and vehicle. By actively acquiring the state information of road objects and pre-determining whether they are threats, intervention and adjustment are made before a potential impact occurs, reducing the risk of vehicle loss of control and the possibility of occupant injury caused by sudden impact.

[0005] According to a first aspect of this application, a vehicle impact active defense method is provided, comprising: acquiring state information of the road surface object for characterizing structural stability before the vehicle contacts the road surface object; determining, based on the state information, whether the road surface object is a threat that will cause an unexpected impact to the vehicle after physical contact; when the road surface object is determined to be the threat, calculating a set of trigger times based on the estimated time of contact between the vehicle and the threat and the inherent response time of each subsystem in a plurality of vehicle subsystems; generating a preventive adjustment command based on the trigger times; and, in response to the preventive adjustment command, triggering corresponding subsystems at the trigger times respectively, so as to coordinately adjust the state of the plurality of subsystems before the vehicle contacts the threat.

[0006] As one possible implementation, obtaining the state information of the road surface object for characterizing structural stability includes: obtaining the appearance structural feature data of the road surface object; obtaining the dynamic response feature data of the road surface object; and fusing the appearance structural feature data and the dynamic response feature data to generate the state information.

[0007] By acquiring and fusing the appearance and structural features and dynamic response features of road objects, the limitations of incomplete information from a single sensor are overcome, enabling more reliable identification of the potential threat status of road objects and providing a more accurate data foundation for subsequent judgments.

[0008] As one possible implementation, the step of fusing the appearance structural feature data and the dynamic response feature data to generate the state information includes: calculating a first risk index reflecting the structural integrity of the road surface object based on the appearance structural feature data; wherein the appearance structural feature data includes the gap width, damage degree, and tilt angle of the road surface object relative to the road surface; calculating a second risk index reflecting the possibility of displacement or deformation of the road surface object under external excitation based on the dynamic response feature data; wherein the dynamic response feature data includes the dynamic change of the depth difference between the road surface object and the road surface; fusing the first risk index and the second risk index to obtain a fusion confidence level as the state information; wherein, based on the state information, determining whether the road surface object is a threat includes: determining that the road surface object is a threat when the fusion confidence level is greater than a preset threshold.

[0009] By quantifying appearance and dynamic features into probability values ​​and comparing them with a threshold using fusion confidence, the threat assessment process becomes objective and repeatable, reducing the probability of false positives and false negatives.

[0010] As one possible implementation, when determining that the road surface object is the threat, calculating a set of trigger times based on the estimated time of contact between the vehicle and the threat, and the inherent response time of each subsystem in the vehicle's multiple subsystems, includes: when determining that the road surface object is the threat, calculating a set of trigger times based on the estimated time of contact between the vehicle and the threat, and the inherent response time of each subsystem in the vehicle's multiple subsystems, and introducing a safety margin; wherein, the safety margin is used to compensate for at least one of sensor delay, communication jitter, or calculation fluctuation.

[0011] By introducing a safety margin into the computational logic at the trigger moment, additional buffer time is reserved for the system response, compensating for sensor latency, communication jitter, and computational fluctuations throughout the entire link from sensing and computation to command issuance. This helps ensure that even in real-world, non-ideal engineering environments, the preventative adjustments of each subsystem can be completed before contact occurs, improving the robustness of timing control and the success rate of proactive defense.

[0012] As one possible implementation, when determining that the road surface object is the threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, the inherent response time of each subsystem in the vehicle's multiple subsystems, and a safety margin. This includes: calculating the first trigger time of the suspension system pre-adjustment command, the second trigger time of the occupant restraint system pre-tightening command, and the third trigger time of the vehicle stability system pre-preparation command based on the estimated time and the inherent response time, and subtracting the safety margin; wherein the first trigger time is earlier than the second trigger time, and the second trigger time is earlier than the third trigger time.

[0013] By specifying the exact timing relationship between the suspension system, restraint system, and vehicle stability system, the suspension system, which has the slowest response, is activated first, followed by the restraint system, and finally the stability system, which has the fastest response, is prepared last, thus forming a progressive and logically rigorous active defense timing chain.

[0014] As one possible implementation, in response to the preventive adjustment command, corresponding subsystems are triggered at the triggering time to coordinately adjust the states of the multiple subsystems before the vehicle comes into contact with the threat. This includes: in response to the preventive adjustment command, adjusting the damping of the suspension system and the preload of the occupant restraint system before the vehicle comes into contact with the threat; and in response to the preventive adjustment command, the vehicle stability system enters a high-alert state; wherein the high-alert state is used to indicate the establishment of braking pressure to brake wheels exhibiting abnormal slip rates after contact.

[0015] By clearly defining the specific objects of coordinated adjustment as suspension damping, occupant restraint preload, and the pre-preparation state of the vehicle stability system, the abstract coordinated adjustment command is transformed into substantive defensive actions for the three key dimensions of vehicle buffering, occupant restraint, and instability prevention. This establishes a three-dimensional active defense system before an impact occurs, which can more comprehensively address the multi-dimensional risks brought about by impacts compared to adjusting only a single system.

[0016] As one possible implementation, adjusting the damping of the suspension system and the pretensioning force of the occupant restraint system includes: adjusting the damping coefficient of the suspension system from a first value to a second value before the vehicle comes into contact with the threat; wherein the second value is higher than the first value; and activating the active pretensioning seat belt motor of the occupant restraint system before the vehicle comes into contact with the threat to retract the seat belt to reduce the gap between the occupant and the seat belt.

[0017] The specific adjustment methods for suspension damping from low to high and the specific actions of seat belt motor rewinding to eliminate gaps are further defined, giving the pre-adjustment of the suspension and restraint system a clear and executable physical implementation path. This ensures that before an impact occurs, the suspension has a stronger energy dissipation capacity to prevent the wheels from lifting off the ground, while the seat belt can eliminate the slack gap between the occupant and the seat to reduce the impact transmission efficiency.

[0018] As one possible implementation, after determining that the road surface object is a threat and before calculating a set of trigger times, the method further includes: determining whether the vehicle meets preset lane change and avoidance conditions; wherein, the preset lane change and avoidance conditions include: the existence of adjacent lanes and the vehicle having a preset safe distance in the current lane to complete the lane change; when the vehicle does not meet the preset lane change and avoidance conditions, calculating a set of trigger times.

[0019] By adding a priority avoidance logic after the threat is identified and before the trigger moment is generated, the vehicle can fundamentally avoid risks by changing lanes when conditions permit, and only initiate active adjustments to the vehicle subsystem when it is impossible to avoid the threat safely, thereby improving the rationality and intelligence of the decision-making.

[0020] As one possible implementation, the method further includes: calculating the slip ratio of at least one wheel after the vehicle comes into contact with the threat; and activating corrective braking to adjust the vehicle's attitude when the slip ratio is greater than a preset safety threshold.

[0021] By monitoring wheel slip ratio in real time after impact and triggering corrective braking, active stabilization control of vehicle attitude is achieved, forming a complete safety closed loop from prediction to prevention to remediation, preventing the risk of secondary loss of control that may be caused by impact.

[0022] According to a second aspect of this application, a vehicle is provided, comprising: a data acquisition unit for acquiring state information of objects on the road surface; and a control unit configured to perform the vehicle impact active defense method as described in the first aspect or any implementation thereof.

[0023] According to a third aspect of this application, a vehicle impact active defense device is provided, comprising: a sensing module, configured to acquire state information of the road surface object characterizing its structural stability before the vehicle contacts the road surface object; a determination module, configured to determine, based on the state information, whether the road surface object is a threat that will cause an unexpected impact to the vehicle after physical contact; a calculation module, configured to calculate a set of trigger times based on the estimated time of contact between the vehicle and the threat, and the inherent response time of each subsystem in a plurality of vehicle subsystems when the road surface object is determined to be the threat; a generation module, configured to generate a preventive adjustment command based on the trigger times; and an adjustment module, configured to, in response to the preventive adjustment command, trigger corresponding subsystems at the trigger times to coordinately adjust the states of the plurality of subsystems before the vehicle contacts the threat.

[0024] According to a fourth aspect of this application, a computer device is provided, the computer device comprising: one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect or any implementation thereof.

[0025] According to a fifth aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing the method as described in the first aspect or any implementation thereof.

[0026] According to a sixth aspect of this application, an electronic device is provided, including a module for performing the method as described in the first aspect or any implementation thereof.

[0027] According to a seventh aspect of this application, a computer program product is provided, comprising program code for performing the method as described in the first aspect or any implementation thereof.

[0028] The vehicle impact active defense method and vehicle provided in this application actively acquire the state information of road objects before the vehicle contacts them. This allows the vehicle to grasp the state information representing the structural stability of the road objects in advance, thereby establishing the ability to perceive potential risks before physical contact and providing the necessary data foundation for subsequent threat assessment and active defense decisions. Based on the acquired state information, the vehicle determines whether a road object is a threat, enabling it to distinguish between potentially dangerous road objects and safe ordinary road objects, avoiding indiscriminate responses and improving the targeting and effectiveness of decisions. After determining that an object is a threat, the inherent differences in the physical response speed of each subsystem are quantitatively analyzed, and the trigger time of each subsystem is calculated to coordinate risk resistance capabilities based on system response delays. When an object is determined to be a threat, a preventative adjustment command is generated, overcoming the limitation of traditional safety systems that can only generate response commands after an impact or accident occurs. This allows for the determination and generation of response plans before the hazard occurs, providing the vehicle with time to actively adjust before physical contact. Responding to preventative adjustment commands and coordinating the states of multiple subsystems before the vehicle comes into contact with a threat, the system enables all systems to be uniformly scheduled and synchronously prepared before the impact occurs, constructing a three-dimensional, multi-layered active defense posture. This achieves a shift from passively enduring impacts to actively preparing for defense, reducing the risk of vehicle attitude loss of control and occupant injury caused by sudden impacts. Attached Figure Description

[0029] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0030] Figure 1 This is a schematic diagram of the architecture of a vehicle impact active defense system provided in an exemplary embodiment of this application.

[0031] Figure 2 This is a schematic flowchart of an exemplary embodiment of the vehicle impact active defense method provided in this application.

[0032] Figure 3 This is a schematic diagram of the structure of an active vehicle impact protection device provided in an exemplary embodiment of this application.

[0033] Figure 4 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0034] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0035] In the field of vehicle driving safety technology, a common technical solution for proactive protection against road impact events is to rely on a single visual sensor to identify road objects and combine this with a passive safety system for post-event response. In practice, a forward-facing camera captures road images, and object detection algorithms are used to accurately locate the position and outline of road objects such as manhole covers. The operating mechanism of this solution is as follows: the camera continuously acquires images, and the processor analyzes whether manhole cover-like objects are present in the images. Once successfully identified, the system does not make further judgments, and the vehicle continues driving as before until the impact occurs. After the impact, the suspension, seat belts, and vehicle stability system activate independently based on the collision or instability signals detected by the sensors. This solution can achieve basic road object recognition at a relatively low cost, while relying on mature passive safety regulations to meet protection requirements under normal operating conditions.

[0036] However, when vehicles encounter potential threats such as loose manhole covers, existing technologies, in order to balance the wide applicability and low latency of visual recognition, inevitably suffer from design flaws such as limited perception dimensions, delayed decision-making response, and isolated subsystem execution, leading to insufficient protective effectiveness. For example, in a scenario where a vehicle runs over a loose manhole cover at high speed, the camera can only identify the presence of the cover but cannot detect whether it is loose before contact. The system only triggers braking or tightens seatbelts after the impact, while the manhole cover's bounce process takes only milliseconds, easily missing the opportunity for post-incident intervention. The suspension system, seatbelt system, and vehicle stability system operate independently, unable to coordinate preparation for the impending impact, easily leading to loss of vehicle control and significant impact on occupants.

[0037] Analysis reveals that the root cause of the problem can be attributed to three levels. At the information level, the relevant technologies rely solely on a single visual modality, only acquiring the static position and outline of objects. They cannot quantitatively assess the structural stability (such as gap width, damage level, and tilt angle) or dynamic behavioral characteristics (such as micro-displacement and vibration frequency), resulting in the system's threat assessment remaining at the existence level rather than the risk level. At the decision-making level, the control logic of the relevant technologies is post-event triggered, requiring sensors to detect impact signals (such as peak acceleration) before intervention is initiated. This response mode means the system cannot reserve sufficient time before the impact occurs to coordinate actuators with different response speeds. At the execution level, the related technologies have independent controllers and communication buses for subsystems such as suspension, seat belts, and braking, lacking unified task planning and precise timing coordination. This means that even if some subsystems receive instructions in advance, they may not be able to complete state preparation at the same moment the impact occurs due to differences in response time.

[0038] Therefore, to overcome the aforementioned contradictions, this invention proposes a vehicle impact active defense method and vehicle. By acquiring the state information of objects on the road before the vehicle contacts them, and based on this information determining whether an object poses a threat that could cause an unexpected impact, the method establishes an active perception and judgment capability before the impact occurs. After determining that an object is a threat, a set of different trigger times are calculated based on the inherent response time of each subsystem to generate preventative adjustment commands, coordinating subsystems with different response times to simultaneously counteract the impact. This transforms the traditional passive impact-bearing single-response mode into a complete active defense chain that actively acquires state information, determines threats, generates contingency plans in advance, and coordinates the execution of adjustments. This improves the vehicle's ability to actively identify, predict, and coordinate defense against threats before contact, and avoids performance degradation caused by perception blind spots, decision lags, and execution silos.

[0039] Figure 1 This is a schematic diagram of the architecture of a vehicle impact active defense system according to an embodiment of the present invention. Figure 1As shown, the vehicle impact active defense system 1 consists of four layers: a perception layer 11, a decision layer 12, an execution layer 13, and a feedback layer 14. These layers interact with each other via a high-speed vehicle bus for data and control commands. The perception layer 11 includes a camera module 111, a lidar module 112, and a vehicle status module 113, used to collect road surface images, point cloud data, and the vehicle's own motion status information in front of the vehicle, and send the processed data to the decision layer 12. The decision layer 12 includes a global monitoring agent 121 and an anti-impact decision agent 122, responsible for receiving information output from the perception layer 11, performing situation assessment and strategy calculation, and generating preventative adjustment commands for each execution subsystem. The execution layer 13 includes an active suspension execution module 131, an active restraint system module 132, and a vehicle stability system module 133, used to adjust suspension characteristics, restrain occupant status, and prepare for braking intervention, respectively. The feedback layer 14 consists of an inertial measurement unit 141, a wheel speed sensor 142, and an acceleration sensor 143, etc., and is used to monitor the actual motion state of the vehicle after the impact occurs, and feed the monitoring results back to the decision layer 12 to form a closed-loop regulation circuit.

[0040] Each sensor module in the perception layer 11 connects to the controller in the decision layer 12 via a dedicated data bus. The two agents in the decision layer 12 share information and coordinate decisions through an internal communication mechanism. The three execution modules in the execution layer 13 independently receive instructions from the decision layer 12 and connect to their respective execution mechanisms. The sensors in the feedback layer 14 transmit real-time data to the decision layer 12 and some execution modules via a low-speed bus or direct hardwired signals to ensure the timeliness of attitude stability control. The data flow of the entire system is as follows: the perception layer 11 uploads environmental and vehicle status data to the decision layer 12; the decision layer 12 calculates the data and then issues timing control instructions to the downstream execution layer 13; after the execution layer 13 completes the action, the feedback layer 14 sends the results back, thus achieving a complete perception-decision-execution-feedback closed loop.

[0041] For example, when the vehicle tires come into contact with a threat, the feedback layer 14 is activated immediately. The inertial measurement unit 141 collects the vehicle's three-axis acceleration and angular velocity in real time; wheel speed sensors 142 collect the instantaneous rotational speed of each wheel to calculate the slip ratio; and acceleration sensors 143 monitor the vehicle's vertical acceleration to quantify the impact intensity. The anti-impact decision-making agent 122 dynamically adjusts subsequent control strategies based on the deviation between the received feedback data (such as actual impact intensity and vehicle yaw rate) and the expected values. For example, if a slight sideslip trend is detected after the impact (abnormal yaw rate), the anti-impact decision-making agent 122 can issue additional torque intervention commands to the vehicle stability system module 133, combining with braking intervention to achieve more precise attitude recovery. This closed-loop feedback mechanism enables the system to cope with uncertainties under non-ideal operating conditions, ensuring vehicle stability.

[0042] Figure 2 This is a flowchart illustrating an exemplary embodiment of the vehicle impact active defense method provided in this application. The method is executed by the vehicle's control unit, which may be part of the vehicle's central domain controller or distributed controller cluster.

[0043] The following text combines Figure 2 This application provides a detailed description of the vehicle impact active defense method provided in the embodiments.

[0044] S210: Before the vehicle comes into contact with a road surface object, acquire state information of the road surface object to characterize its structural stability.

[0045] During operation, the vehicle continuously monitors the road ahead via camera and lidar modules. The vehicle status module acquires real-time driving status information, including but not limited to vehicle speed (V), heading angle (φ), and vehicle load (M), via a CAN (Controller Area Network) bus. This data is transmitted to the decision-making layer for subsequent spatiotemporal synchronization alignment, calculation of estimated contact time, and adjustment of defense strategy parameters.

[0046] As one possible implementation, state information can be multidimensional data characterizing whether a road surface object (such as a manhole cover) is in a stable or loose state. Examples include appearance and structural feature data acquired through visual sensors, dynamic response feature data acquired through lidar, or a fusion of both. State information can also be a probability value or confidence level used to quantify the likelihood of an object becoming loose. Acquiring state information must be completed before any part of the vehicle (such as wheels) makes physical contact with the road surface object, thus gaining processing time for subsequent decision-making and adjustments—the golden window. Fully utilizing this window allows for a shift from passively accepting damage to actively defending against it.

[0047] In some embodiments, state information can be generated by acquiring the appearance and structural feature data of the road surface object, acquiring the dynamic response feature data of the road surface object, and fusing the appearance and structural feature data and the dynamic response feature data.

[0048] For appearance and structural feature data, the camera module in the perception layer is configured to acquire road surface images. The control unit processes the images by running a lightweight AI model (such as an improved YOLOv8n) deployed on the onboard computing platform to identify and extract the appearance and structural features of road objects. These features mainly include information related to the static structure of the object. For example, taking a manhole cover as an example, the control unit identifies the manhole cover's location, outline, and texture, analyzes the gap width (w_gap) between the manhole cover edge and the surrounding road surface, the degree of asphalt damage around the manhole cover (d_crack), and the tilt angle (θ_tilt) of the manhole cover surface relative to the horizontal road surface. These appearance and structural feature data intuitively reflect the physical state of the road objects.

[0049] Dynamic response feature data reflects the real-time behavior of objects in their environment. After the LiDAR module is activated, it performs a high-precision point cloud scan of the road surface area identified by the camera. The control unit analyzes multi-frame point cloud data to extract dynamic response features. Dynamic response feature data can include the dynamic change in the depth difference between the road surface and the road surface (i.e., whether the manhole cover undergoes slight displacement due to airflow or other vehicles passing by during vehicle approach, expressed as the average depth difference Δd_mean and the standard deviation of the depth difference Δd_std). Dynamic response feature data is the basis for determining whether an object is loose.

[0050] Furthermore, relying solely on a single type of data (such as vision or LiDAR alone) carries the risk of misjudgment or missed judgment. To obtain more comprehensive and reliable judgments, a fusion decision unit is deployed within the control unit. This fusion decision unit employs a Bayesian fusion model, taking appearance structure feature data and dynamic response feature data as input. By assigning dynamic weights to data from different sources (e.g., increasing visual weights in good lighting conditions and increasing LiDAR weights in adverse weather conditions), a comprehensive state information is ultimately generated. This multi-dimensional information fusion improves the perception system's anti-interference capability and accuracy.

[0051] As one possible approach, before fusing appearance structure feature data and dynamic response feature data, the data collected by each sensor in the perception layer can be preprocessed. For example, precise timestamp alignment and spatial coordinate transformation can be performed on image frames acquired by the camera module, point cloud frames acquired by the LiDAR module, and vehicle status data acquired by the vehicle status module to ensure that the data from each modality are fused in the same spatiotemporal coordinate system. After completing the spatiotemporal synchronization alignment, the process of acquiring appearance structure feature data and dynamic response feature data is initiated.

[0052] To further optimize the accuracy and objectivity of threat identification, in some embodiments, a first risk index is calculated based on appearance and structural feature data; wherein, appearance and structural feature data includes the gap width, damage degree, and tilt angle of the road object relative to the road surface; a second risk index is calculated based on dynamic response feature data; wherein, dynamic response feature data includes the dynamic change of the depth difference between the road object and the road surface; the first risk index and the second risk index are fused to obtain a fused confidence score as state information; when the fused confidence score is greater than a preset threshold, the road object is determined to be a threat.

[0053] The first risk indicator is a quantitative indicator reflecting the structural integrity of road surfaces, calculated based on appearance and structural feature data. It is a probability value output by a weighted logistic regression model after inputting features such as gap width, damage degree, and tilt angle. The second risk indicator reflects the probability of displacement or deformation of road surfaces under external stimuli. It is a quantitative indicator reflecting the possibility of loosening of road surfaces, calculated based on dynamic response feature data. For example, it is a probability value output by a logistic regression model after inputting features such as the average depth difference, standard deviation of depth difference, or amplitude of micro-displacement.

[0054] First, the system can receive real-time road surface images captured by a front-view high-definition camera (resolution ≥1920×1080, frame rate ≥30fps). A lightweight AI model (such as an improved YOLOv8n) deployed on the vehicle computing platform is then used to identify the manhole cover's location and extract a visual feature vector V=[w_gap, d_crack, θ_tilt, a_area, t_texture]. Here, w_gap is the gap width (mm) between the manhole cover edge and the road surface, reflecting the fit between the manhole cover and the road; d_crack is the equivalent diameter (mm) of the surrounding asphalt damage area, reflecting the aging degree of the road surface around the manhole cover; θ_tilt is the tilt angle (°) of the manhole cover surface relative to the road surface, reflecting whether the manhole cover is tilted; a_area is the percentage of abnormal area on the manhole cover surface (%), reflecting the structural integrity of the manhole cover; and t_texture is the texture feature difference index (dimensionless), reflecting the consistency of the texture between the manhole cover and the road surface. These appearance and structural feature data are then input into a pre-defined probability calculation model, which uses a weighted logistic regression algorithm. The calculation formula is as follows: In the formula, This indicates the primary risk indicator. This is the bias term, which can take a value of -2.5. The weight coefficients of each feature can be set to... =0.35、 =0.25、 =0.20、 =0.15、 =0.05, obtained through training with a large number of labeled samples. This calculation process transforms ambiguous visual observations (e.g., large gaps) into a clear and comparable primary risk indicator. This ensures that the evaluation process is objective and repeatable.

[0055] Secondly, while processing the appearance and structural feature data, dynamic response feature data representing the dynamic behavior of road surface objects are also processed. For example, high-precision point cloud data of the manhole cover area collected by the lidar module is processed to extract the lidar feature vector L=[Δd_mean, Δd_std, n_points, m_motion, t_vib], where Δd_mean is the average depth difference variation of the manhole cover area (mm), reflecting the height difference between the manhole cover and the road surface; Δd_std is the standard deviation of the depth difference variation (mm), reflecting the smoothness of the manhole cover surface; n_points is the point cloud density (points / m²), reflecting the detection confidence; m_motion is the amplitude of the manhole cover's micro-motion displacement (mm), obtained through multi-frame point cloud registration, reflecting the dynamic loosening characteristics of the manhole cover; and t_vib is the vibration frequency feature (Hz), reflecting the vibration mode of the manhole cover caused by passing vehicles. Based on this, a logistic regression model is also used to calculate the second risk indicator. The calculation formula is: in, This indicates the second risk indicator. This indicates the change in the average depth difference in the manhole cover area. This indicates the amplitude of the manhole cover's micro-displacement. This represents the weighting coefficient. It can take the value -3.2. It can take the value 0.4. The value can be 1.2. Quantifying the multidimensional features detected by lidar into numerical probabilities can directly quantify the dynamic attribute of loosening by combining multidimensional information such as depth, micro-motion, and vibration, regardless of lighting conditions.

[0056] Finally, the fusion decision unit fuses the calculated first and second risk indicators. For example, a Bayesian fusion model is used, incorporating environmental conditions and sensor characteristics to calculate visual weights and LiDAR weights separately. Visual weights Determined by light intensity, weather conditions, and target distance, it is represented as =f(light, weather, distance), where the light parameter reflects the current ambient illumination level (e.g., normalized to 0-1, 1 being the optimal light), the weather parameter characterizes the degree of interference from rain, fog, snow, etc., on visual imaging (e.g., 0 for worst weather, 1 for clear weather), and the distance parameter is the distance between the target manhole cover and the vehicle (e.g., normalized to 0-1, 1 being the closest, at which point the visual resolution is highest). LiDAR weights. Determined by both point cloud density and vibration amplitude, it is expressed as =g(point cloud density, vibration amplitude), where point cloud density reflects the number of points effectively scanned by the lidar in the manhole cover area (e.g., normalized to 0-1, 1 is dense), and vibration amplitude characterizes the relative intensity of vibration of the manhole cover during vehicle movement (e.g., normalized to 0-1, 1 is severe vibration, at which point the lidar echo is more susceptible to interference).

[0057] The fusion confidence score is calculated using the following formula, and this fusion confidence score is used as the state information: In the formula, This represents the fusion confidence level, and N represents the number of valid observation frames. This represents the convergence coefficient, which can take a value of 0.3. Indicates visual weight, Indicates the weight of the lidar. This indicates the primary risk indicator. This represents the second risk indicator. This fusion method combines the advantages and disadvantages of vision and lidar under different environmental conditions, dynamically adjusts the weights, reduces the dependence on a single sensor, and enhances the stability of the fusion results by utilizing the number of observation frames.

[0058] Understandably, in cost-constrained scenarios, LiDAR can be omitted, and a purely vision-based solution can be adopted. For example, a camera module can continuously capture multiple frames of images of the manhole cover ahead. The anti-impact decision-making agent can estimate the relative loosening displacement of the manhole cover by comparing the pixel displacement of the manhole cover edge in adjacent frames and compensating for it with the vehicle's own motion information, thus achieving low-cost loosening detection. Alternatively, 4D millimeter-wave imaging radar can be used to acquire high-density point clouds and height information of the manhole cover area. This radar can replace LiDAR for micro-motion detection, and is lower in cost and unaffected by adverse weather conditions such as rain, fog, and snow. Furthermore, with the widespread adoption of smart road infrastructure, signals containing manhole cover health status information sent by roadside units can be directly received via V2X communication technology, thus eliminating the need for onboard sensors to perform autonomous identification and judgment.

[0059] See also Figure 2 S220: Based on state information, determine whether a road surface object is a threat that will cause an unexpected impact on the vehicle after physical contact.

[0060] Threat objects refer to road surface objects that may cause loss of vehicle control or impact injury to occupants (such as loose manhole covers, raised road joints, and protruding obstacles).

[0061] After acquiring the status information, it is compared with preset judgment logic or thresholds. Methods for implementing the judgment function include: at the software level, the control unit runs a judgment model that receives the status information acquired by S210 as input and outputs a Boolean value (yes / no) or a level (such as high risk or low risk). For example, when the status information is a value representing the loosening confidence level, if the value is greater than a preset threshold, the system determines that the road surface object is a threat and triggers subsequent active defense procedures.

[0062] For example, the fusion confidence score It is compared with a preset threshold T, which can be set to 0.85. When the system detects a loose manhole cover ahead, it identifies it as a high-risk, threatening object. Setting preset thresholds ensures clear and repeatable decision-making, making threat assessment more scientific and accurate, and reducing false alarms and missed alarms.

[0063] See also Figure 2 S230: When a road surface object is determined to be a threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, as well as the inherent response time of each subsystem in the vehicle's multiple subsystems.

[0064] When generating preventative adjustment commands, the anti-impact decision-making agent in the control unit executes the following logic: First, it calculates a set of trigger times based on the expected time of contact between the vehicle and the threat and the inherent response time of each subsystem.

[0065] That is, based on the vehicle's current speed V and the distance S from the manhole cover ahead, the estimated time of contact with the threat, T1 = S / V, is calculated. Based on the inherent response times of the systems (e.g., suspension system response time Δt_susp is 150ms, seatbelt system response time Δt_seat is 80ms, and vehicle stability system response time Δt_esp is 50ms), the trigger time of each subsystem is calculated. For example, to optimize the timing logic of the coordinated adjustment of multiple subsystems, based on the estimated time and inherent response time, the trigger times of the suspension system pre-adjustment command (T1-150ms), the occupant restraint system pre-tightening command (T1-80ms), and the vehicle stability system pre-preparation command (T1-50ms) are calculated separately.

[0066] This specific timing design is based on the actual response of several systems. Due to its physical characteristics (such as the time required to adjust the magnetorheological fluid), the suspension system has a relatively long response time and needs to be activated earliest to allow sufficient time (150ms) to increase the damping coefficient from comfort mode (approximately 2000 Ns / m) to sport mode (approximately 6000 Ns / m), preparing to absorb the upcoming large impact. Subsequently, the occupant restraint system is triggered at T1-80ms. At this point, before the occupants have perceived any abnormality, the seatbelt motor activates, preemptively eliminating the gap between the occupants and the seats / seatbelts before the vehicle makes rigid contact with the manhole cover. Finally, the vehicle stability system is triggered at T1-50ms. This system responds the fastest, entering a high-alert state and pre-establishing braking pressure in just 50ms. By setting this sequence from slow to fast, a logically rigorous active defense sequence is constructed, fully leveraging the synergistic protective effectiveness of each subsystem.

[0067] Based on these calculated trigger times, preventative adjustment commands containing timing information are generated. When coordinating the adjustment of multiple vehicle subsystems, the corresponding subsystems are triggered based on the trigger times and the preventative adjustment commands.

[0068] For example, a pre-adjustment command is sent to the suspension controller at time T1-150ms; and a pre-tensioning command is sent to the seatbelt controller at time T1-80ms. This time-sharing triggering mechanism based on precise timing helps overcome the problem of asynchronous actions caused by simultaneous command issuance, ensuring that adjustments are completed before the contact time T1, regardless of the subsystem's response speed.

[0069] As one possible implementation, the actual response time of each subsystem can be calculated based on its inherent response time and communication latency, combined with vehicle speed and load compensation factors. Suspension system response time: Δt_susp=t_susp_response+t_comm_delay+δ_susp(V,M); where t_susp_response ranges from 20 to 30 ms, t_comm_delay ranges from 5 to 10 ms, and δ_susp(V,M) is the speed and load compensation factor; The seat belt system response time Δt_seat = t_seat_response + t_comm_delay + δ_seat(V), where t_seat_response ranges from 40 to 50 ms, t_comm_delay ranges from 5 to 10 ms, and δ_seat(V) is the speed compensation factor. The Electronic Stability Program (ESP) system's Δt_esp = t_esp_response + t_comm_delay, where t_esp_response ranges from 5 to 10 ms and t_comm_delay ranges from 5 to 10 ms.

[0070] Based on the dynamic response time calculated above, the trigger time of each subsystem is calculated: suspension trigger time T1-Δt_susp, seat belt trigger time T1-Δt_seat, and ESP trigger time T1-Δt_esp.

[0071] In some embodiments, when a road surface object is determined to be a threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, the inherent response time of each subsystem in the vehicle's multiple subsystems, and a safety margin t_margin. This safety margin compensates for at least one of sensor delay, communication jitter, or computational fluctuations, ensuring that the system can complete all adjustments before physical contact. The formula for calculating the safety margin t_margin is: t_margin = max(10ms, 5ms + 0.001 × V), where V is the vehicle's current speed (in m / s). This formula ensures a safety margin of at least 10ms and appropriately increases the margin as the vehicle speed increases to cope with greater dynamic uncertainties at higher speeds.

[0072] By introducing a safety margin, when calculating the actual trigger time of each subsystem, the first trigger time of the suspension system pre-adjustment command, the second trigger time of the occupant restraint system pre-tightening command, and the third trigger time of the vehicle stability system pre-preparation command can be calculated based on the expected time and inherent response time, minus the safety margin. The first trigger time is earlier than the second trigger time, and the second trigger time is earlier than the third trigger time. This compensates for unavoidable sensor delays, communication jitter, and computational fluctuations throughout the entire chain, improving the robustness of timing control and the success rate of active defense.

[0073] Based on this, the actual instruction triggering time of each subsystem is calculated using the following formula: T_susp_cmd=T1-Δt_susp-t_margin; T_seat_cmd=T1-Δt_seat-t_margin; T_esp_cmd=T1-Δt_esp-t_margin; Where T1 is the estimated time of contact between the vehicle and the threat, and Δt_susp, Δt_seat, and Δt_esp are the inherent response times of the suspension system, occupant restraint system, and vehicle stability system, respectively. T_susp_cmd is the first trigger time for the suspension system pre-adjustment command, T_seat_cmd is the second trigger time for the occupant restraint system pre-tightening command, and T_esp_cmd is the third trigger time for the vehicle stability system pre-preparation command.

[0074] See also Figure 2 S240: Generate preventative adjustment instructions based on the trigger time.

[0075] Once a road object is determined to be a threat, a set of preventative adjustment commands are generated based on the calculated trigger time. During the generation of these commands, the global monitoring agent and the anti-impact decision agent in the decision-making layer work collaboratively. Upon receiving a high-risk assessment from the perception layer, the global monitoring agent first evaluates whether it is safe to change lanes to avoid the threat. If avoidance is not possible, a defense mode is activated, and information such as the distance S between the vehicle and the threat, the current vehicle speed V, and the vehicle's load M are transmitted to the anti-impact decision agent. Based on this information and the inherent response time of each subsystem, the anti-impact decision agent calculates the optimal defense strategy, including the accurate trigger time and adjustment parameters, thus generating the preventative adjustment commands. These preventative adjustment commands are not simply triggered simultaneously; rather, they contain specific control parameters and timing information for different vehicle subsystems (such as suspension, seat belts, and vehicle stability systems). For example, the commands can specify at what time the suspension system adjusts its damping coefficient to what value, or at what time the seat belt system activates its pretensioning motor. Preventive adjustment instructions are used to calculate a coordination scheme through preset logic, which can bring each subsystem to its optimal defense state before an impact occurs.

[0076] See also Figure 2 S250: In response to a preventative adjustment command, the corresponding subsystems are triggered at the trigger time to coordinate the state of multiple subsystems before the vehicle comes into contact with a threat.

[0077] The control unit sends the generated preventative adjustment commands to the corresponding subsystem actuators via a high-speed vehicle bus (such as CAN FD or vehicle Ethernet). For example, upon receiving the command, the suspension system begins adjusting damping and stiffness before the wheels contact the manhole cover; the seatbelt pretensioning system activates the motor within the same time window to rewind the seatbelt. Through precise timing coordination among multiple systems, the vehicle shifts from passively absorbing impacts to actively defending against them, reducing the negative impacts of the impact and overcoming the vehicle attitude loss and occupant injury problems caused by system islanding and response lag in related technologies.

[0078] In some embodiments, in response to a preventive adjustment command, the damping of the suspension system and the preload of the occupant restraint system are adjusted before the vehicle comes into contact with a threat; in response to the preventive adjustment command, the vehicle stability system enters a high alert state; wherein the high alert state is used to indicate the establishment of braking pressure to brake wheels exhibiting abnormal slip rates after contact.

[0079] For example, after receiving a pre-adjustment command, the suspension system increases the suspension damping coefficient from comfort mode (approximately 2000 Ns / m, the first value) to sport mode (approximately 6000 Ns / m, the second value) within ≤30ms. Simultaneously, it adjusts the air spring stiffness to make the suspension exhibit firm, energy-absorbing characteristics, and eliminates the gap between the occupant and the seat / seatbelt. A high-alert state is used to indicate the establishment of braking pressure. For example, after receiving a command, the vehicle stability system pre-builds pressure in the master cylinder (e.g., 10 bar), but does not generate actual braking force, remaining only in a preparatory state. Thus, substantial preparations are made in three dimensions—buffering the vehicle body, restraining the occupants, and stabilizing the vehicle—before an impact occurs.

[0080] Furthermore, before the vehicle makes contact with the threat, the damping coefficient of the suspension system is adjusted from a first value (e.g., 2000 Ns / m in comfort mode) to a second value (e.g., 6000 Ns / m in sport mode), where the second value is higher than the first. This quantitative adjustment enhances the suspension's energy dissipation capacity during impact. Before the vehicle makes contact with the threat, the active pretensioning seatbelt motor of the occupant restraint system is activated, retracting the seatbelt (e.g., 30-50 mm) to reduce the gap between the occupant and the seatbelt. By actively retracting the seatbelt, slack is eliminated, allowing the occupant to move synchronously with the vehicle during impact, preventing separation of the occupant and vehicle due to inertia and secondary impact injuries.

[0081] In some embodiments, after determining that a road object is a threat and before calculating a set of trigger times, a decision branch step is introduced, namely, determining whether the vehicle meets preset lane change avoidance conditions; wherein, the preset lane change avoidance conditions include: the existence of an adjacent lane and the vehicle having a preset safe distance in the current lane to complete the lane change; when the vehicle does not meet the preset lane change avoidance conditions, a set of trigger times is calculated.

[0082] The preset lane change avoidance conditions are evaluated by the global monitoring agent, including whether there are adjacent lanes and whether the vehicle has a preset safe distance within the current lane to complete the lane change. For example, the global monitoring agent detects lane lines (solid / dashed lines) through cameras, obtains the position and speed of other vehicles in adjacent lanes through radar or cameras, and calculates the minimum distance D required for a safe lane change. min If the current distance S > D minIf there is no collision risk in adjacent lanes, it indicates that avoidance is possible, and a lane-change avoidance instruction is generated and executed first. If safe avoidance is not possible (e.g., in a solid line area, with vehicles in adjacent lanes, or insufficient distance), a preventative adjustment instruction is generated. By adding an avoidance-priority judgment logic, a better and lower-risk safety strategy can be provided, improving overall intelligence and user experience.

[0083] In some embodiments, the feedback layer activates after the vehicle comes into contact with a threat. The feedback layer, composed of an inertial measurement unit, wheel speed sensors, and acceleration sensors, monitors the vehicle's actual motion in real time, including wheel speeds, yaw rate, and lateral acceleration. Based on this sensor data, the control unit first calculates the slip ratio of at least one wheel. When the slip ratio is determined to be greater than a preset safety threshold, corrective braking is immediately activated to precisely brake the slipping wheel, dynamically adjusting the vehicle's attitude. Simultaneously, the feedback layer transmits the monitoring results after the impact back to the decision layer, enabling the control unit to dynamically adjust subsequent control strategies based on the actual effects, thus forming a complete closed-loop control circuit.

[0084] For example, when a vehicle hits the ground after running over a manhole cover, if the slip ratio of one wheel exceeds a safety threshold due to a sudden change in ground adhesion, the vehicle stability system can establish braking pressure within ≤10ms to precisely brake the slipping wheel. Simultaneously, it coordinates with the motor to output torque, correcting the vehicle's yaw moment and preventing fishtailing. This closed-loop adjustment enables real-time monitoring and dynamic stability control of the vehicle's attitude, helping to avoid the risk of subsequent loss of control due to impact.

[0085] In other embodiments, the logic for generating preventative adjustment instructions at the decision layer can be modified. For example, the perception results can be directly input into a deep neural network (such as a Transformer model), which then directly outputs the control parameters and timing of each actuator, thereby replacing the explicit logical decision-making process of the distributed agent. Alternatively, for the specific timing parameters used to trigger each subsystem (such as the delay time of the suspension, seat belts, and ESP), reinforcement learning algorithms can be used to perform self-learning optimization under different vehicle speeds and vehicle operating conditions, thereby finding the optimal timing window for each specific scenario.

[0086] In other embodiments, the specific actions of the execution layer in coordinating the states of multiple subsystems can be implemented in various ways. For example, in terms of the suspension system, a hydraulic active suspension can be used. When an impact is anticipated, the wheels are actively lifted by hydraulic cylinders, causing them to perform a step-over action at the moment of contact with the manhole cover instead of directly crushing it, thereby physically avoiding the impact. In terms of occupant protection, when an impact is anticipated, the backrest angle and seat cushion tilt angle of the seat can be adjusted to place the occupant in a zero-gravity posture most conducive to withstanding vertical impact, thus dispersing the force on the spine. If the impact intensity exceeds a preset threshold, the system can link with the airbag control unit to deploy the seat pelvic airbag or central airbag, providing additional cushioning protection for the occupant. In terms of vehicle stability, in vehicles equipped with in-wheel motors, the independent torque control capability of the four wheels can be used to achieve faster and more precise vehicle stability correction than traditional ESP braking intervention, in order to cope with the risk of loss of control after contact.

[0087] The present invention also provides a vehicle. The vehicle includes a data acquisition unit and a control unit. The data acquisition unit may include the camera module and lidar module, etc., as described in the foregoing embodiments, for acquiring state information of objects on the road surface. The control unit may be the vehicle's central controller or a distributed controller cluster, configured to execute the vehicle impact active defense method described above.

[0088] Below is an example of an application scenario: a vehicle equipped with a vehicle impact active protection system is traveling normally at 60 kilometers per hour on a main road in a city. The driver is unaware that a manhole cover ahead has become noticeably loose due to repeated vehicle traffic. The vehicle's forward-facing high-definition camera continuously captures images of the road ahead, and a lightweight AI model analyzes and identifies the location and outline of the manhole cover. Simultaneously, the system automatically extracts structural features such as the width of the gap between the manhole cover's edge and the road surface, the degree of damage to the surrounding asphalt, and the tilt angle of the manhole cover. Next, a LiDAR system is activated to perform a high-precision point cloud scan of the manhole cover area. Through multi-frame point cloud registration, the system calculates the dynamic changes in the depth difference between the manhole cover surface and the road surface, capturing the minute displacements caused by the vibrations of passing vehicles. The control unit inputs the visual feature data and the LiDAR dynamic response feature data into a Bayesian fusion model to comprehensively calculate the looseness confidence score of the manhole cover. When the fusion confidence score consistently exceeds a threshold of 0.85, the system determines the manhole cover ahead as a high-risk threat.

[0089] At this point, the global monitoring agent begins to assess the current environment to determine if it is safe to change lanes to avoid the manhole cover. However, a vehicle is traveling parallel in an adjacent lane, and the current lane markings are solid, making a safe lane change impossible. The anti-impact decision-making agent then activates its defense mode, calculating the estimated time to reach the manhole cover based on the current vehicle speed and distance. Based on the inherent response times of each subsystem, a precise set of trigger times is calculated, with the first trigger time for the suspension system's pre-adjustment command being the earliest, followed by the second trigger time for the occupant restraint system's pre-tightening command, and the third trigger time for the vehicle stability system's pre-preparation command being the latest. This rigorous timing design ensures that systems with different response speeds can coordinate appropriately before the impact occurs.

[0090] 150ms before the wheel contacts the manhole cover, the active suspension module receives a pre-adjustment command. Within 30ms, it increases the suspension damping coefficient from 2000 Ns / m in comfort mode to 6000 Ns / m in sport mode, while simultaneously adjusting the air spring stiffness to give the suspension a firm and energy-absorbing characteristic. 80ms before the wheel contacts the manhole cover, the active seatbelt motor activates, retracting the seatbelt 30-50mm within 50ms, completely eliminating the gap between the occupant and the seat / seatbelt. 50ms before the wheel contacts the manhole cover, the vehicle stability system enters a high-alert state, and the master cylinder pre-builds braking pressure. When the wheel hits the ground after passing the manhole cover, the vehicle experiences an impact, and the suspension system effectively absorbs the impact energy, helping to prevent the wheels from lifting off the ground. After landing, wheel speed sensors monitor the rotational speed of each wheel in real time, and the control unit quickly calculates the slip ratio. If any wheel experiences an abnormal slip ratio, the vehicle stability system can build braking pressure within ten milliseconds to precisely brake the slipping wheel, while simultaneously coordinating with the motor to output torque to correct the vehicle's yaw moment.

[0091] In this scenario, the active protection system effectively mitigated the impact of the loose manhole cover. Occupants experienced only slight vibrations and no noticeable sense of being thrown off. The vehicle maintained stable posture, without skidding or fishtailing, and continued to travel smoothly through the section of road, enhancing both the driving experience and safety.

[0092] Figure 3 This is a schematic diagram of the structure of a vehicle impact active protection device provided in an exemplary embodiment of this application, as shown below. Figure 3As shown, the vehicle impact active defense device 3 includes: a sensing module 31, used to acquire state information of the road surface object to characterize its structural stability before the vehicle comes into contact with the road surface object; a determination module 32, used to determine whether the road surface object is a threat that will cause an unexpected impact to the vehicle after physical contact based on the state information; a calculation module 33, used to calculate a set of trigger times based on the expected time of contact between the vehicle and the threat, and the inherent response time of each subsystem in the vehicle's multiple subsystems when the road surface object is determined to be a threat; a generation module 34, used to generate preventive adjustment commands based on the trigger times; and an adjustment module 35, used to trigger the corresponding subsystems at the trigger times in response to the preventive adjustment commands, so as to coordinately adjust the state of multiple subsystems before the vehicle comes into contact with the threat.

[0093] As one possible implementation, the perception module 31 can be configured to: acquire the appearance and structural feature data of road surface objects; acquire the dynamic response feature data of road surface objects; and fuse the appearance and structural feature data and the dynamic response feature data to generate state information.

[0094] As one possible implementation, the perception module 31 can also be configured to: calculate a first risk indicator based on appearance and structural feature data; wherein the appearance and structural feature data includes the gap width, damage degree, and tilt angle of the road surface object relative to the road surface; calculate a second risk indicator based on dynamic response feature data; wherein the dynamic response feature data includes the dynamic change of the depth difference between the road surface object and the road surface; fuse the first risk indicator and the second risk indicator to obtain a fusion confidence level as state information; wherein the determination module 32 is configured to determine that the road surface object is a threat when the fusion confidence level is greater than a preset threshold.

[0095] As one possible implementation, the calculation module 33 can be configured to: when determining that a road surface object is a threat, calculate a set of trigger times based on the estimated time of contact between the vehicle and the threat, the inherent response time of each subsystem in the vehicle's multiple subsystems, and introduce a safety margin; wherein the safety margin is used to compensate for at least one of sensor delay, communication jitter, or calculation fluctuation.

[0096] As one possible implementation, the calculation module 33 can also be configured to: calculate the first trigger time of the suspension system pre-adjustment command, the second trigger time of the occupant restraint system pre-tightening command, and the third trigger time of the vehicle stability system pre-preparation command based on the expected time and the inherent response time, and after subtracting the safety margin; wherein the first trigger time is earlier than the second trigger time, and the second trigger time is earlier than the third trigger time.

[0097] As one possible implementation, the adjustment module 35 can be configured to: adjust the damping of the suspension system and the preload of the occupant restraint system in response to a preventive adjustment command before the vehicle comes into contact with a threat; and, in response to the preventive adjustment command, the vehicle stability system enters a high alert state; wherein the high alert state is used to indicate the establishment of braking pressure to brake the wheels exhibiting abnormal slip ratios after contact.

[0098] As one possible implementation, the adjustment module 35 can be configured to: adjust the damping coefficient of the suspension system from a first value to a second value before the vehicle comes into contact with a threat; wherein the second value is higher than the first value; and before the vehicle comes into contact with a threat, activate the active pretensioning seat belt motor of the occupant restraint system to rewind the seat belt to reduce the gap between the occupant and the seat belt.

[0099] As one possible implementation, the vehicle impact active defense device 3 may include: determining whether the vehicle meets preset lane change avoidance conditions; wherein the preset lane change avoidance conditions include: the existence of adjacent lanes and the vehicle having a preset safe distance in the current lane to complete the lane change; when the vehicle does not meet the preset lane change avoidance conditions, calculating a set of trigger times.

[0100] As one possible implementation, the vehicle impact active defense device 3 may also include: calculating the slip ratio of at least one wheel after the vehicle comes into contact with a threat; and activating corrective braking to adjust the vehicle's attitude when the slip ratio is greater than a preset safety threshold.

[0101] An electronic device includes: a processor; a memory for storing processor-executable instructions; and a processor for executing the vehicle impact active defense method described in the embodiments of this application.

[0102] Below, for reference Figure 4 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0103] Figure 4 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0104] like Figure 4 As shown, the electronic device 40 includes one or more processors 41 and a memory 42.

[0105] The processor 41 may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device 40 to perform desired functions.

[0106] The memory 42 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 41 may execute the program instructions to implement the vehicle impact active defense methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0107] In one example, the electronic device 40 may also include an input device 43 and an output device 44, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0108] When the electronic device is a standalone device, the input device 43 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0109] In addition, the input device 43 may also include, for example, a keyboard, a mouse, etc.

[0110] The output device 44 can output various information to the outside, including determined distance information, direction information, etc. The output device 44 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0111] Of course, for the sake of simplicity, Figure 4 Only some of the components of the electronic device 40 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 40 may include any other suitable components depending on the specific application.

[0112] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0113] A computer-readable storage medium stores a computer program for executing the vehicle impact active defense method described in the embodiments provided in this application.

[0114] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0115] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for active protection against vehicle impact, characterized in that, include: Before the vehicle comes into contact with a road surface object, the state information of the road surface object used to characterize its structural stability is acquired. Based on the state information, determine whether the road surface object is a threat that will cause an unexpected impact on the vehicle after physical contact; When the road surface object is determined to be the threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, as well as the inherent response time of each subsystem in the vehicle's multiple subsystems. A preventative adjustment command is generated based on the triggering time; In response to the preventative adjustment command, corresponding subsystems are triggered at the triggering time to coordinately adjust the state of the multiple subsystems before the vehicle comes into contact with the threat.

2. The vehicle impact active defense method according to claim 1, characterized in that, The step of obtaining the state information of the road surface object used to characterize structural stability includes: Obtain the appearance and structural feature data of the road surface object; Obtain the dynamic response characteristic data of the road surface object; The state information is generated by fusing the appearance and structural feature data and the dynamic response feature data.

3. The vehicle impact active defense method according to claim 2, characterized in that, The process of fusing the appearance structural feature data and the dynamic response feature data to generate the state information includes: Based on the aforementioned appearance and structural feature data, a first risk index reflecting the structural integrity of the road surface object is calculated; wherein, the appearance and structural feature data includes the gap width, degree of damage, and tilt angle of the road surface object relative to the road surface; Based on the dynamic response characteristic data, a second risk index is calculated to reflect the possibility of displacement or deformation of the road surface object under external excitation; wherein, the dynamic response characteristic data includes the dynamic change of the depth difference between the road surface object and the road surface. The first risk indicator and the second risk indicator are fused to obtain the fused confidence score as the state information; The determination of whether a road surface object is a threat, based on the aforementioned state information, includes: When the fusion confidence level is greater than a preset threshold, the road surface object is determined to be a threat.

4. The vehicle impact active defense method according to claim 1, characterized in that, When determining that the road surface object is the threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, and the inherent response time of each subsystem in the vehicle's multiple subsystems, including: When the road surface object is determined to be the threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, the inherent response time of each subsystem in the vehicle's multiple subsystems, and a safety margin is introduced. The safety margin is used to compensate for at least one of sensor delay, communication jitter, or computational fluctuations.

5. The vehicle impact active defense method according to claim 4, characterized in that, When determining that the road surface object is the threat, a set of trigger times is calculated based on the estimated time of contact between the vehicle and the threat, the inherent response time of each subsystem in the vehicle's multiple subsystems, and by introducing a safety margin. These trigger times include: Based on the estimated time and the inherent response time, and subtracting the safety margin, the first trigger time of the suspension system pre-adjustment command, the second trigger time of the occupant restraint system pre-tightening command, and the third trigger time of the vehicle stability system pre-preparation command are calculated respectively. Wherein, the first triggering time is earlier than the second triggering time, and the second triggering time is earlier than the third triggering time.

6. The vehicle impact active defense method according to claim 1, characterized in that, In response to the preventative adjustment command, the corresponding subsystems are triggered at the triggering time to coordinately adjust the states of the multiple subsystems before the vehicle comes into contact with the threat, including: In response to the preventative adjustment command, the damping of the suspension system and the preload of the occupant restraint system are adjusted before the vehicle comes into contact with the threat. In response to the preventative adjustment command, the vehicle stability system enters a high alert state; wherein the high alert state is used to indicate the establishment of braking pressure to brake wheels exhibiting abnormal slip rates upon contact.

7. The vehicle impact active defense method according to claim 6, characterized in that, The adjustment of the damping of the suspension system and the adjustment of the preload of the occupant restraint system include: Before the vehicle comes into contact with the threat, the damping coefficient of the suspension system is adjusted from a first value to a second value; wherein the second value is higher than the first value. Before the vehicle comes into contact with the threat, the active pretensioning seat belt motor of the occupant restraint system is activated to retract the seat belt to reduce the gap between the occupant and the seat belt.

8. The vehicle impact active defense method according to claim 1, characterized in that, After determining that the road surface object is a threat and before calculating a set of trigger times, the method further includes: Determine whether the vehicle meets the preset lane change and avoidance conditions; wherein, the preset lane change and avoidance conditions include: there is an adjacent lane and the vehicle is within a preset safe distance in the current lane to complete the lane change; When a vehicle does not meet the preset lane change and avoidance conditions, a set of trigger times is calculated.

9. The vehicle impact active defense method according to claim 1, characterized in that, The method further includes: After the vehicle comes into contact with the threat, the slip ratio of at least one wheel is calculated; When the slip ratio is greater than a preset safety threshold, corrective braking is activated to adjust the vehicle's attitude.

10. A vehicle, characterized in that, include: The acquisition unit is used to acquire the state information of objects on the road surface; A control unit configured to perform the vehicle impact active defense method according to any one of claims 1 to 9.

Images