1989 results about "Vehicle dynamics" patented technology

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The invention discloses an unmanned vehicle intelligent obstacle avoidance method and system based on a multi-modal sensor, and relates to the field of automatic driving and intelligent traffic. According to the technical key points of the invention, data of a laser radar and a depth camera are aligned through a space-time registration module; the laser radar point cloud is preprocessed, obstacle point cloud is segmented, fusion clustering is carried out in combination with three-dimensional semantic seed points output by a depth camera, and an obstacle cluster with a semantic tag is generated; and calculating a three-dimensional directed bounding box (OBB) of an obstacle, dynamically adjusting a safety distance according to the speed of the vehicle, expanding the OBB of the vehicle, and carrying out collision detection by adopting a separation axis theorem. When a collision risk is detected, the system sequentially triggers a first-level early warning instruction and a second-level emergency braking instruction, and safe and efficient unmanned vehicle dynamic obstacle avoidance is achieved. According to the method, the advantages of multiple sensors are fully utilized, the obstacle detection precision and the response speed are effectively improved, and reliable technical support is provided for application of the unmanned driving technology in a complex road environment.

The invention relates to the technical field of automatic driving, and particularly provides an automatic driving vehicle track re-planning method, which comprises the following steps of: firstly, evaluating stability risk and defining three states of vehicle stability through real-time vehicle dynamic parameters, and designing intervention and exit criteria of track re-planning according to the three states; when the vehicle driving is evaluated to be in an unstable state, generating a virtual obstacle by using prediction information of a path tracking module, and converting a dynamic stability boundary into a spatial constraint; performing trajectory re-planning by adopting nonlinear model predictive control, and adjusting the weight between the path tracking precision and the obstacle avoidance demand according to the relative position of the virtual obstacle in combination with a dynamic weight adjustment mechanism; and finally, generating an optimized trajectory containing stability constraint, and realizing high-precision tracking through linear MPC. The method can effectively balance the path tracking precision and the vehicle stability under a complex working condition, and prevents the vehicle from entering a dynamic unstable region.

The invention relates to the technical field of forklift driving stability control, in particular to a forklift driving stability control system based on ultrasonic and laser composite ranging, which comprises a composite ranging module, a multi-modal data fusion module, a stability prediction and control module, an execution mechanism control module and an online learning system. The composite distance measuring module collects surrounding environment information and corrects motion errors through a dynamic compensation algorithm; the multi-modal data fusion module dynamically adjusts the weight of the sensor based on an environmental interference factor, and realizes space-time calibration in combination with extended Kalman filtering; the stability prediction and control module is fused with the vehicle dynamic characteristics to predict the rollover risk and generate a hierarchical control instruction; the actuating mechanism control module drives actuating components such as an active suspension and an electronic differential mechanism through a layered framework. The method can adapt to complex working conditions, the prediction precision and fault-tolerant capability are improved, safety and efficiency are balanced, the maintenance cost is reduced, and the method is suitable for various forklifts.

The invention belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

The invention discloses an automatic driving control method based on passenger motion sickness perception and comfort, which comprises the following steps: acquiring and preprocessing vehicle dynamic performance data, passenger physiological signals and subjective scores, extracting dynamic characteristics such as linear acceleration, angular velocity, jerk and omnidirectional acceleration, and physiological characteristics such as electrocardio and galvanic skin; feature fusion and standardization are carried out; a passenger motion sickness perception and comfort score prediction model is constructed; an individualized motion sickness threshold value is constructed based on a model output result, a motion sickness risk score is calculated in combination with future short-time vehicle motion characteristics, and perspective estimation of the motion sickness risk is achieved; and executing a hierarchical processing strategy according to the sensitivity typing and the risk level, and dynamically adjusting the vehicle driving style, the sound-light-temperature environment in the vehicle and the voice prompt. According to the automatic driving control method based on the motion sickness sensing and comfort of the passenger, real-time identification and active intervention of the motion sickness state of the passenger can be achieved, and the riding comfort and the man-machine cooperation level of an automatic driving vehicle are improved.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention belongs to the field of traffic control, and relates to a high-risk scene-oriented risk, dynamics and intention collaborative trajectory prediction method, which comprehensively utilizes an outward risk clue, vehicle dynamics characteristics and driving intention information (historical trajectory characteristics), and constructs a priori enhanced risk attention mechanism and a three-body interaction micrograph module to predict the risk, dynamics and intention collaborative trajectory of a high-risk scene. Early perception of potential risks is realized; meanwhile, a multi-scale dynamic time sequence encoder is adopted to capture the dynamic change of the vehicle from short-term disturbance to long-term behavior trend; an explicit intention recognition auxiliary task is introduced, and semantic constraint and optimization are carried out on the multi-modal trajectory; through collaborative modeling of the modules, the trajectory prediction precision and robustness of the automatic driving system under complex high-risk situations such as sudden cut-in and sudden brake can be remarkably improved, more reliable prior support is provided for a subsequent decision and control module, and therefore the driving safety of a vehicle in a complex traffic environment is effectively improved.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention belongs to the technical field of reusable aircraft control, and relates to an intelligent fault diagnosis and control method for a reusable aircraft. According to the method, a control-oriented aircraft dynamics model is established, a neural network adaptive fault observer and a particle swarm optimization algorithm are fused, and high-precision tracking control over the attitude and trajectory of the aircraft is achieved. When faults such as performance loss of an actuator and unmeasurable deviation occur, fault information can be estimated quickly and accurately, the reconstructed fault information is integrated into control law design, and it is guaranteed that the aircraft still keeps stable flight in a serious fault state. Meanwhile, the method can effectively deal with composite disturbances such as pneumatic uncertainty, elastic modal coupling and external environment interference, and improves the real-time performance and accuracy of fault diagnosis by means of high-precision joint online estimation. In addition, the particle swarm optimization algorithm further enhances the overall performance and adaptive ability of the control system, so that the aircraft operates more efficiently and stably in the flight process.

The invention discloses a light passenger car chassis steering stability self-adaptive control method, which comprises the following steps of: collecting multi-dimensional working condition parameters in real time, and covering 12 types of key parameters such as a steering wheel rotating angle and a wheel speed; after preprocessing and abnormity diagnosis, double models are adopted to estimate the dynamic state of the vehicle in parallel; dynamically setting a control target, and realizing personalized control in combination with driver style recognition; the control quantity is optimized through multi-mode strategy dynamic switching and steering and braking system cooperative control; and actuator dynamic compensation, scenarized self-learning and multi-stage fault handling mechanisms are introduced. According to the method, the complex working condition adaptability is improved, the response speed is controlled, the subsystem cooperation efficiency is improved, the system robustness and the long-term use performance are enhanced, and the driving safety of the light bus is effectively guaranteed.

A method is for improving a vehicle-dynamics-related stability of a utility vehicle, wherein the utility vehicle has a lift axle. The method includes: determining a current ground speed of the utility vehicle; determining a stability-critical speed of the utility vehicle; comparing the current ground speed with the stability-critical speed; determining a lift status of the lift axle of the utility vehicle; and lowering the lift axle of the utility vehicle if the lift status represents a raised lift axle and the current ground speed is greater than or equal to the stability-critical speed. A device is for improving a vehicle-dynamics-related stability of a utility vehicle.

The invention discloses a trajectory tracking and stability cooperative control method and system for a four-wheel independent steering vehicle. The method comprises the steps that firstly, a four-wheel independent steering vehicle dynamics model and a tire model are built according to vehicle parameter information; secondly, drawing a vehicle phase plane graph based on a kinetic model and a tire model to obtain a vehicle stability constraint relation and dividing a stability region; then a dual-mode adaptive model prediction controller is constructed based on the phase plane stability region and the vehicle state; and finally, constructing a system model for trajectory tracking and stability coordination control of the four-wheel independent steering automobile by combining a lower-layer corner torque distributor. The stability constraint relation and control parameters of the vehicle can be adjusted in real time according to the state parameters of the vehicle, and the trajectory tracking precision and stability of the four-wheel independent steering vehicle are comprehensively improved.

The invention discloses an unmanned vehicle dynamic obstacle avoidance method and system based on near-end strategy optimization, and the method comprises the steps: firstly constructing a differential drive unmanned vehicle dynamic model, and defining an action space; then designing a composite observation space, and constructing a mixed reward function; and then a control strategy is trained based on a near-end strategy optimization PPO algorithm in combination with a course learning mechanism. According to the designed composite observation space, multi-modal environment representation is provided, the time sequence motion trend of the dynamic obstacle is captured by adopting the frame stacking technology, and the decision continuity is improved. The dynamic obstacle avoidance performance of the unmanned vehicle is improved through staged course learning and a mixed reward mechanism, so that the unmanned vehicle can realize dynamic obstacle avoidance.

The invention discloses a curve driving collision early warning method and system based on multi-risk field fusion, and the method comprises the steps: 1, collecting vehicle dynamics data in real time, and carrying out the short-time prediction; step 2, geometric modeling and rasterization of a road; 3, constructing a geometric risk field; 4, constructing a kinetic energy risk field; 5, constructing a behavior risk field; step 6, constructing a comprehensive risk field; step 7, collision risk judgment; and step 8, generating an active intervention strategy. Collaborative perception and risk prediction of vehicles, roads and driving behaviors are realized through multi-risk field fusion, and potential collision hidden dangers of curve driving are identified in advance; a vehicle dynamics model and risk correction based on road adhesion are introduced to realize accurate risk assessment of the vehicle; multi-time window trajectory prediction and behavior modeling are adopted, so that the recognition capability of complex driving behaviors is improved; a closed-loop system from risk prediction and decision making to active control is realized, and the safety and stability of the vehicle driving in the curve scene are effectively improved.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention belongs to the field of intelligent vehicle-mounted technology, and particularly relates to a multifunctional vehicle-mounted sensing detection system based on multi-source information fusion, which comprises multi-source information acquisition, high-precision space-time synchronization and heterogeneous data preprocessing. A heterogeneous sensing system with feature level deep fusion, vehicle state dynamic performance evaluation and adaptive decision and risk evaluation capabilities is constructed, and a driving decision is dynamically adjusted through fusion perception and vehicle performance. The vehicle state and dynamic performance evaluation module is constructed by deeply integrating vehicle internal state data acquired by a vehicle-mounted diagnosis system into a perception fusion and decision planning process, and the core contradiction of disjunction of a perception result and a vehicle dynamic performance strategy is effectively solved. The system can evaluate key performance parameters such as power, braking, steering and the like of the vehicle and potential faults of the key performance parameters in real time, and the driving strategy and the safety margin are dynamically adjusted in the self-adaptive decision and risk evaluation module according to the actual physical limitation of the vehicle.

The invention discloses a man-machine dynamic cooperative steering control method integrating reinforcement learning and a game theory, and aims to optimize dynamic weight distribution of a driver and a controller so as to guarantee driving safety and path tracking precision of a man-machine co-driving vehicle. The invention relates to the field of intelligent driving. The system comprises an expected path module, a reinforcement learning module, a non-cooperative game module and a man-machine co-driving vehicle module. The expected path module is used for generating an expected path sequence of the driver and the controller and an expected path at the current moment; the reinforcement learning module receives the expected path at the current moment, the vehicle dynamic information and the optimal rotation angle of the driver and the controller, and training is carried out to generate a driver control weight; and the non-cooperative game module combines the driver control weight, the expected path sequence of the driver and the controller and the state information of the vehicle, optimizes the optimal steering angle of the driver and the controller through the non-cooperative game, and realizes the cooperative steering control of the man-machine co-driving vehicle.

The invention relates to the technical field of unmanned vehicle path planning, and discloses an unmanned vehicle dynamic path planning method based on multi-source sensor fusion. The method comprises the following steps: an environment state dynamic coding step: generating an environment state coding vector through a dynamic coding model; a prediction path generation step of generating a prediction path sequence of the unmanned vehicle; a multi-dimensional path difference analysis step: performing multi-dimensional difference comparison on the predicted path sequence and real-time environment data to generate a path difference coefficient tensor; a risk area probability positioning step: generating a risk probability distribution diagram through space environment topology analysis based on the path difference coefficient tensor; a dynamic path cost calculation step of calculating a dynamic path cost vector in combination with the risk probability distribution diagram and the environment state coding vector; a path planning strategy optimization step: optimizing a path planning strategy through a strategy learning model; and a real-time dynamic path generation step: generating a real-time dynamic path based on the optimized strategy.

The invention discloses an electric power steering fault diagnosis and self-adaption system based on torque offset, which relates to the technical field of fault diagnosis and comprises a data acquisition unit, a model establishment unit, a torque offset evaluation unit, a fault judgment unit and a power output adjustment unit. According to the method, the return-to-power torque model is constructed based on the vehicle dynamics law and experience adjustment, the model parameters are optimized to obtain the return-to-power torque model, meanwhile, the real-time output torque of the EPS system is calculated according to the steering wheel torque signal, the deviation coefficient of the EPS system is comprehensively calculated, and the accuracy of the EPS system is improved. The deviation coefficient is evaluated according to a preset fault grading interval, a multi-grade fault judgment mechanism is established according to the change rule of the deviation coefficient, dynamic health evaluation of the EPS system motor performance is achieved, the evaluation serves as the basis of control strategy adjustment, the output assistance value can be dynamically adjusted, return control can be enhanced or a safety mode can be switched, and the reliability of the EPS system motor performance is improved. Therefore, the redundancy control capability and the driving safety are improved.

Narrow vehicles have multiple advantages but tend to have a limited application because they are roll unstable. The use of flywheels to increase their stability is a known method but the operation range, complexity, performance and cost usually limit the application of these vehicles. Stability and agility enhancement system and method enabling the application of stabilizing forces from flywheels and the steering of the trajectory concurrently and cooperatively with the driver's steering and assistance enable the benefit of reduced cost and complexity while providing enhanced performances, intuitive control, safety and operation range.

The invention relates to a vehicle trajectory tracking control method considering dynamic motion requirements. Comprising the following steps: 1, establishing a nonlinear vehicle dynamics model by comprehensively considering longitudinal motion, lateral motion and yawing motion of a vehicle; 2, searching saddle node bifurcation points with qualitative change of the stability state of the vehicle through a particle swarm optimization algorithm and a Lyapunov criterion, and constructing a dynamic stability domain boundary of the vehicle under the change of the longitudinal vehicle speed and the road adhesion coefficient; and 3, designing a vehicle multi-target trajectory tracking controller based on a nonlinear model predictive control theory, quantitatively evaluating the stability state of the vehicle by adopting a stability index, and meanwhile, designing a controller built-in parameter adjustment strategy to match the dynamic motion requirements of the distributed driving electric vehicle in different stability states. According to the method, the driving safety and the movement comfort of the distributed driving electric automobile are effectively ensured, and a set of solution is provided for realizing the optimal cooperative control of the chassis subsystem of the automatic driving automobile.

The invention discloses a vehicle stability control method, system and device, a medium and a program product, and relates to the technical field of vehicle dynamic control, and the method comprises the steps: calculating a vehicle speed, a pavement friction coefficient, a slip rate, a load transfer rate and a preview path curvature; the vehicle speed, the road friction coefficient, the load transfer rate and the preview path curvature serve as input, the yaw velocity deviation and the slip rate serve as reward signals, and a yaw velocity dynamic threshold value is generated; according to the actual yaw velocity, the expected yaw velocity and the yaw velocity dynamic threshold, obtaining a judgment result that the current vehicle is under-steering or over-steering, and generating a control strategy in combination with a vehicle driving form; and the optimal braking torque adjustment amount is calculated with the minimum yaw velocity deviation and the slip rate as the optimization target, and the front and rear shaft driving torque is obtained according to the deviation between the actual side slip angle and the expected side slip angle. The self-adaptive dynamic environment and the differentiated adaptive driving form are realized, and the safety and the controllability under the complex working condition are improved.

The invention provides a modular and multifunctional karst collapse physical simulation experiment platform. Comprising a rigid frame, a bearing device, a moving device, a loading device, a lifting device, a pile foundation construction dynamic load simulation device, a ground traveling dynamic load simulation device, a rainfall simulation device, a computer vision monitoring device, an underground water pumping and draining simulation device and an underground space excavation disturbance simulation device. The moving device is arranged on the bearing device, the lifting device, the loading device and the pile foundation construction dynamic load simulation device are arranged on the moving device, the travelling crane dynamic load simulation device is connected with a traction device of the moving trolley, and the camera shooting monitoring device and the rainfall simulation device are arranged on the rigid frame through a support. According to the method, key disaster-causing conditions in the urban environment are fully considered, the working condition scene coverage is wide, and the applicability is high; modularized design is adopted as a whole, all the modules can be flexibly loaded according to requirements, the real environment can be simulated more truly, and the catastrophe evolution process of surface collapse can be reproduced.

The invention relates to the technical field of unmanned aerial vehicle monitoring and trajectory processing, and discloses a low-altitude unmanned aerial vehicle dynamic trajectory tracking and predicting method based on multi-base-station cooperation. The method comprises the steps that signal parameters are obtained through multi-base-station collaborative observation, and a multi-modal position prediction set is generated; and performing classification and scoring according to the spatial distribution characteristics of the candidate points and the historical track points, and screening out an optimal prediction position point. And inputting the optimal prediction point and the historical trajectory into a generative model, and dynamically adjusting the number of trajectory points by analyzing the point distribution probability to form a preliminary smooth trajectory. And performing physical feasibility verification and fine adjustment on the trajectory according to kinematics constraints to obtain a final smooth continuous trajectory. And matching degree calculation is carried out by fusing alternative trajectories generated by a multi-kinematic model, and the most probable target trajectory is identified. According to the method, the robustness of trajectory prediction and the structural rationality of the generated trajectory in a complex observation environment are improved.

The invention provides a vehicle dynamics performance analysis method and system considering suspension parameter performance degradation, and relates to the technical field of railway vehicle dynamics, and the method comprises the steps: building a nonlinear Maxwell hydraulic damper model; establishing a vehicle system dynamics model in simulation software based on a vehicle dynamics theory; integrating the nonlinear Maxwell hydraulic damper model into a vehicle system dynamic model, and establishing a vehicle system nonlinear dynamic model considering suspension parameter performance degradation; setting model parameters of the nonlinear Maxwell hydraulic damper to obtain a simulation result; and analyzing the dynamic performance evaluation index of the vehicle so as to complete dynamic performance evaluation of the vehicle under the conditions of actual service mileage and environment temperature. The method effectively reflects the influence of suspension parameter degradation on the dynamic performance of the vehicle, improves the precision of a dynamic simulation model of the railway vehicle and the reasonability of an analysis method, and has important theoretical research significance for innovative design and intelligent operation and maintenance of the railway vehicle.

The invention relates to the technical field of traffic control, and provides a traffic adaptive control method and system based on vehicle state perception and an intelligent road network. The method comprises the steps that a vehicle state sensing module is installed on a target vehicle, and a vehicle state sensing multi-dimensional data stream is collected; an intelligent road network module is adopted to monitor a road network multi-dimensional data flow; carrying out fusion modeling on the vehicle state perception multi-dimensional data flow and the road network multi-dimensional data flow to generate a dynamic traffic scene model; traffic operation trend parameters are predicted based on a dynamic traffic scene model, and driving dynamic decision and traffic adaptive control are performed on a target vehicle through the parameters, so that the problem that the state of the target vehicle and the road network condition cannot be acquired and accurately analyzed in real time in the existing traffic control technology is solved. The technical problem that traffic flow and traffic adaptive control effects are poor due to the fact that vehicle state sensing and intelligent road network fusion modeling are solved, and the technical effects that accurate prediction and control of traffic flow and vehicle dynamic states are achieved through vehicle state sensing and intelligent road network fusion modeling, so that traffic management is optimized, and driving safety is improved are achieved.

The invention is suitable for the technical field of automatic driving, and provides an automatic driving vehicle blind area track prediction and planning method based on Transform, and the method comprises the steps: firstly constructing a key entrance point, calculating a shielding region, and then generating and maintaining a belief state of a ghost target in combination with a risk score; then, real and ghost targets are coded into Token in a unified mode, the Token is input into Transform for interactive modeling, and multi-modal track distribution is output; and finally, a variable anisotropic repulsive force potential field is constructed based on the predicted risk, and a safety control instruction is obtained through optimization solution and hard safety filtering. According to the method, continuous probabilistic estimation and interactive reasoning of the potential risk of the blind area are realized, a flexible defensive track can be generated according to the risk confidence and the dynamic state of the vehicle, and the traffic efficiency is guaranteed while the safety is improved.

The invention provides a vehicle driving state estimation method and system in a centralized driving mode, and relates to the technical field of electric automobiles, and the method comprises the following steps: determining wheel dynamic parameters based on sensor detection information and a seven-degree-of-freedom dynamic model related to wheels, and combining a preset Dugoff tire model to obtain initial tire force parameters; a vehicle-mounted infrared camera is used for obtaining road surface image information, the road surface image information is analyzed through a deep convolutional neural network based on semantic segmentation and a vehicle dynamics inversion method, an adaptive fusion mechanism is introduced for adjustment, and a target road surface adhesion coefficient is determined; correcting the initial tire force parameter by using the target road adhesion coefficient to obtain a target tire force parameter; inputting the current sensor detection information and the target tire force parameter into a self-adaptive unscented Kalman filter to obtain vehicle driving state information; the vehicle driving state information comprises the longitudinal speed, the transverse speed, the yaw velocity and the side slip angle of each tire.

The invention provides a vehicle working condition virtual debugging simulation method based on multi-modal perception fusion, and relates to the technical field of digital twinning, and the method comprises the steps: achieving the precise fusion of multi-modal perception data through the synchronous collection of visual, radar and acoustic signals and the unification of a time reference and a coordinate system; constructing a vehicle dynamics state model based on mutual information weighting and recursive filtering, and obtaining dynamically consistent state vectors; combining space-time modeling and robust coding to generate driving characteristics capable of being physically explained, and inputting the driving characteristics into a simulation environment; in the digital twinborn model, parameter adaptive tracking is realized through online correction and joint recursive updating; and closed-loop feedback debugging is formed by using the health index and the fault probability, so that virtual and real consistent and dynamically adjustable vehicle working condition simulation and intelligent debugging are realized. According to the method, fusion of multi-mode sensing data can be realized, so that the simulation model dynamically tracks the working condition of the vehicle.