496 results about "Vehicle behavior" patented technology

The invention discloses an AR-fused remote driving vehicle virtual-real interaction hardware-in-the-loop test system, particularly relates to the technical field of automatic driving test, and is used for solving the problems of inaccurate coupling between a virtual scene and a real vehicle behavior and lack of AR prompt response evaluation. The method comprises the following steps: firstly, constructing a dynamic obstacle intention-driven prediction model based on time series data of a multi-modal sensor, and generating a trajectory probability distribution and risk thermodynamic diagram; then, space-time alignment of the virtual accident scene and the real environment is achieved through a dynamic binding algorithm, and the virtual-real shielding priority of an AR interface is dynamically adjusted; by simulating abnormal disturbance of a vehicle actuator, synchronously collecting control and watching responses of a driver, and extracting obstacle avoidance path deviation degree and takeover timeliness parameters; and finally, separating and compensating virtual and actual residual errors based on a path deviation index, realizing online correction of a virtual scene attitude and a dynamic trajectory, constructing a closed-loop optimization mechanism, and improving the precision and stability of a test system.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention relates to the technical field of intelligent driving, and discloses a user service recommendation method for a vehicle-mounted scene, and the method comprises the steps: collecting the vehicle-using behavior data of a user, carrying out the correlation marking of the intention of the user through a multi-dimensional marking system, carrying out the vectorization, and storing the vectorized intention into a vector database; capturing real-time behavior data of a user to generate a real-time behavior vector; based on the vector database, generating an intention context corresponding to a real-time behavior vector through a hybrid retrieval strategy; and generating a prompt project based on the intention context, inputting a large model output service recommendation result, and pushing the result to the user. By integrating mechanisms such as data annotation, vector retrieval and large model reasoning, the problem of long cold start period depending on long-term buried point data learning in the prior art is solved, potential intentions are quickly matched, the system effective time is shortened, and the service recommendation accuracy based on the user intentions is effectively improved. The invention further discloses a user service recommendation device for the vehicle-mounted scene, the vehicle and a computer readable storage medium.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention discloses a tunnel entrance dynamic light and signal control system based on a visual algorithm, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of poor light control when the visual field of a tunnel entrance suddenly changes. According to the method, a dynamic sensing mechanism based on visual feature understanding and multi-strategy linkage is constructed, continuous monitoring and intelligent control over the driving visual adaptation state of the tunnel portal are achieved, multi-dimensional visual features are extracted through image collection and preprocessing, visual risk scores are generated, visual field sudden change and traffic abnormity are recognized in combination with a sliding window, and the driving visual adaptation state of the tunnel portal is obtained. A risk response instruction is output, illumination adjustment and signal linkage control are driven, then vehicle behavior continuity, parking probability and blind area shielding factors are fused, speed limiting, guiding or warning signals are dynamically issued, and visual model fine adjustment and strategy updating are performed based on execution feedback. And finally, illumination and signal adaptive optimization under cross-time and multi-environment conditions is realized in combination with a control scene library, and the traffic safety and control intelligence level are improved.

The invention belongs to the technical field of automatic driving, and relates to a virtual simulation dynamic path test method based on a fixed trajectory of an accident scene, which comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing the data to generate point cloud information and visual information, and extracting vehicle parameter information, vehicle behavior information, environment information and traffic participant abnormal behavior information; the vehicle behavior information comprises a fixed track; s2, a virtual simulation scene is constructed, a virtual three-dimensional scene model is constructed according to the point cloud data and the visual information, and parameters of the virtual three-dimensional scene model are set according to the vehicle parameter information, the vehicle behavior information, the environment information, the traffic participant abnormal behavior information and the traffic rules; s3, testing by taking the fixed track as a reference track, and performing dynamic path adjustment by utilizing a path planning algorithm in an automatic driving system during testing; and S4, analyzing the test data, evaluating from multiple dimensions, generating an analysis report, and optimizing suggestions. The problem of inaccurate test results is solved.

The invention discloses an unmanned aerial vehicle behavior recognition method based on multi-modal fusion and a space-time enhancement network, and relates to the technical field of computer vision and artificial intelligence, and the method comprises the steps: extracting human body joint point coordinates frame by frame from an input unmanned aerial vehicle video through employing a lightweight attitude estimation model, and generating an initial skeleton sequence; performing enhancement processing on the initial skeleton sequence, and inputting the enhanced skeleton sequence into a double-branch space-time enhancement network to perform space-time feature extraction; the method comprises the following steps: detecting and segmenting objects in a scene from an unmanned aerial vehicle video by utilizing a grouping visual model, and obtaining a pixel-level object mask and a semantic category thereof; constructing a space-time dynamic graph network based on the detected object; performing cross-modal fusion on the spatio-temporal features and the spatio-temporal dynamic graph; and classifying the fused features, and outputting a final recognition result. According to the method, the behavior recognition robustness in the low-quality video is remarkably improved, interference caused by shielding and multi-view change is effectively relieved, and the ability of understanding human-object interaction semantics is enhanced.

The invention relates to the technical field of parking resource scheduling, in particular to an intelligent parking resource scheduling system based on multi-source data analysis, which realizes full-scale digital mapping from vehicle individual behaviors and parking lot flow fields to a regional traffic network by constructing microscopic-mesoscopic-macroscopic three-level parking twin bodies and fusing dynamic and static multi-source data. Through scale conversion and data interaction among the three-level model, dynamic characteristics of parking resources in space and time dimensions are accurately captured, a high-fidelity and fine-grained digital twinborn base is provided for scheduling decision making, splitting of local optimization and global collaboration in traditional scheduling is broken through, the accuracy and foresight of resource allocation are supported, and the scheduling efficiency is improved. Based on dynamic construction of an algorithm strategy library and twinborn simulation evaluation, the scheme takes a multi-dimensional scheduling value as a core to realize closed-loop optimization of algorithm combination-effect rehearsal-strategy optimization-dynamic adjustment.

The invention provides a trajectory prediction method and device based on behavior intention of an unmanned aerial vehicle, a device and a medium, and the method comprises the steps: employing a random forest model to screen multi-source state information of the unmanned aerial vehicle, obtaining flight state key features, and completing feature importance evaluation and precise screening; building a multi-classification maneuvering intention recognition model by adopting a support vector machine based on the flight state key features, and obtaining probability distribution of various maneuvering actions of the unmanned aerial vehicle based on the multi-classification maneuvering intention recognition model; a neural network trajectory prediction model of a coding-decoding structure with a bidirectional gating circulation unit is constructed, probability distribution of various maneuvering actions of the unmanned aerial vehicle is introduced, the probability distribution is used as key semantic constraints to be embedded into a trajectory prediction algorithm, a technical link of multi-link collaborative optimization is formed, and a prediction result of a future trajectory of the unmanned aerial vehicle is obtained. And high-precision prediction of the track of the unmanned aerial vehicle in a complex environment is effectively realized.

Recent location and control information received from “lead” vehicles that traveled over a segment of land, sea, or air is captured to inform, via aggregated data, subsequent “trailing” vehicles that travel over that same segment of land, sea, or air. The aggregated data may provide the trailing vehicles with annotated road information that identifies obstacles. In some embodiments, at least some sensor control data may be provided to the subsequent vehicles to assist those vehicles in identifying the obstacles and / or performing other tasks. Besides, obstacles, the location and control information may enable determining areas traveled by vehicles that are not included in conventional maps, as well as vehicle actions associated with particular locations, such as places where vehicles park or make other maneuvers.

A computer system for generating a scenario to be run in a simulation environment for testing the behaviour of an autonomous vehicle, the computer system comprising: a rendering component configured to: generate display data for causing a display to render a graphical user interface comprising an image of a driving environment and one or more agents within the driving environment; a parameter generator configured to generate in memory a user-defined parameter set responsive to user input defining the parameter set; and an expression manager configured to store in memory a user-defined expression set, responsive to user input defining the expression set, wherein each expression of the expression set is a user-defined function of one or more parameters of the parameter set; and a scenario generator configured to record the scenario in a scenario database; wherein the graphical user interface is configured to provide multiple agent fields for controlling the behaviour of the one or more agents when the scenario is run in a simulation environment, wherein each agent field is modifiable to associate therewith either a parameter of the user-defined parameter set or an expression of the user-defined expression set; and wherein the recorded scenario comprises the driving environment, the one or more agents, the user-defined parameter set, the user-defined expression set, and any user-defined associations between (i) the multiple agent fields and the user-defined parameter set and (ii) the multiple agent fields and the user-defined expression set, wherein each parameter associated with an agent field is controllable to directly modify an agent behaviour, and each parameter that is included in expression associated with an agent field is controllable to indirectly modify an agent behaviour.

The invention relates to the technical field of intelligent parking management, and discloses a curb parking dynamic charging and fee evasion prevention and control method based on multi-dimensional data fusion, which comprises the following steps: a data fusion reconstruction step: receiving a video stream, sensing a parking space state and reporting data through mobile inspection, performing fusion processing through space-time alignment and a confidence decision algorithm, and obtaining a data fusion result; outputting a standard parking event sequence; a dynamic charging decision-making step of matching a charging strategy model including time-phased elastic rate parameters according to the time-space attribute of the event sequence, calculating the cost and verifying the parking rights and interests in real time, and generating a parking order; and a fee evasion risk real-time prevention and control step: constructing a vehicle behavior portrait feature vector based on the event sequence and the historical order, obtaining a real-time risk level through a pre-trained risk grading model, and triggering a cascade response disposal action according to the real-time risk level, including exit interception and recovery of a high-risk vehicle. According to the invention, the charging accuracy is improved, and the rate dynamic adjustment and the active and accurate prevention and control of the fee evasion behavior are realized.

The invention relates to the technical field of multi-modal time sequence, and discloses a traffic participant intention prediction method and device based on multi-modal time sequence attention, and the method comprises the steps: collecting multi-modal original traffic data in a traffic scene; preprocessing the multi-modal original traffic data to obtain standardized multi-modal data; performing feature extraction on the standardized multi-modal data to obtain a multi-dimensional feature sequence; performing dynamic weighted fusion and semantic association learning on the multi-dimensional feature sequence through a time sequence attention model to obtain a fused feature sequence; performing traffic participant intention probability prediction based on the fused feature sequence to obtain an intention prediction result containing an intention category and a corresponding confidence coefficient; and transmitting the intention prediction result to a decision planning module of the automatic driving system, wherein the decision planning module obtains a vehicle behavior decision strategy adaptive to the current traffic scene based on the intention category and the corresponding confidence coefficient.

The invention relates to an unmanned aerial vehicle threat intention prediction method and device and a storage medium, and is applied to the technical field of anti-unmanned aerial vehicles. The method specifically comprises the steps that through multi-modal deep feature fusion and deep adversarial learning, the system can extract deeper and more abstract unmanned aerial vehicle behavior mode features from multi-source heterogeneous data, so that accurate prediction of the real threat intention of the unmanned aerial vehicle is achieved, the limitation that a traditional method is only based on surface feature judgment is overcome, and the accuracy of the unmanned aerial vehicle threatening intention prediction is improved. The transformation of threat assessment from post-event analysis to pre-event prediction is realized; in the deep adversarial learning framework, various complex unmanned aerial vehicle threat behavior modes including disguise, interference and novel attack strategies can be simulated, and meanwhile, the recognition capability is continuously improved in adversarial training, so that the system can still keep high accuracy and low false alarm rate when facing unknown or variable threats, and the safety of the system is improved. And the robustness and adaptability of the system are obviously enhanced.

The invention provides an expressway lane dynamic optimization management method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: firstly, collecting traffic flow, environment and vehicle credit data through high-speed key node multi-source sensing equipment and a data center, and fusing to generate a time-space fusion data set; inputting the data into a space-time diagram convolution network model, dynamically mapping a road topology into a variable weight directed graph, and generating future lane-level traffic flow, vehicle speed and occupancy rate prediction data through convolution operation; inputting the prediction data into a hierarchical reinforcement learning model, generating a single-vehicle differential management and control instruction in combination with vehicle credit data, and determining an optimal strategy through digital twinborn simulation; then, a modularized lane controller is used for executing a strategy and collecting feedback data; and finally, inputting feedback data into a causal inference engine to analyze the effect, optimizing the reinforcement learning model, and dynamically updating credit data according to vehicle behavior data.

The invention provides an intelligent traffic cone barrel anti-collision early warning method based on binocular vision, and aims to improve the safety of a road construction operation area. And accurate data are provided for subsequent detection and analysis through a binocular camera high-precision calibration technology based on circle detection. In combination with a data acquisition and enhancement technology, high-quality training data is provided for an improved RT-DETR algorithm, and the accuracy and efficiency of vehicle identification are significantly improved. A binocular vision depth perception and StrongSORT multi-target tracking algorithm is utilized to realize real-time speed measurement and distance measurement of a vehicle, and accurate input is provided for track prediction. Based on a GAN multi-modal trajectory prediction technology, a plurality of future trajectories are generated, and a multi-level early warning decision is realized through a comprehensive collision risk index (CRI). According to the method, the limitation of an existing prediction method in a complex road scene is effectively solved, the vehicle behavior intention is predicted and analyzed through the multi-modal trajectory, and reliable early warning information can be provided under various complex road conditions.

A system for infrastructure and environmental mapping and automated vehicle behavior modification includes a plurality of sensors of an autonomous vehicle and an autonomy computing system. The sensors capture sensor data representing an environment in which the autonomous vehicle is operating. The processor receives, from the sensors, first sensor data representing the environment in which the autonomous vehicle is operating at a first time, the first sensor data including image or video data. The processor also detects a difference between the first sensor data and historical sensor data at a location within the environment, and, based on the detected difference, stores an incident record indexed to the location relative to a stored map of the environment. When one or more adjustment criteria associated with the location are satisfied, the processor initiates one or more remedial actions associated with operation of the autonomous vehicle within the environment.

The invention provides a vehicle trajectory prediction system embedded with a spatial-temporal feature fusion strategy and a control method thereof. The system comprises a data time sequence decomposition processing module; a future feature reasoning module; a cross-time motion mixer module; a future interactive attention predictor module; time sequence decomposition is carried out on vehicle trajectory data through time sequence coding, short-term dynamic features and long-term trend features in the vehicle trajectory data are obtained, obtained combined feature information provides input for historical and future trajectory feature interactive modeling and trajectory prediction, and meanwhile a model is helped to achieve long-term prediction of future trajectories; the interaction of complex spaces is considered, a dynamic shuffling mechanism and a channel attention mechanism are combined, the channel attention mechanism helps a model pay attention to important interaction features, the dynamic shuffling mechanism enhances information exchange between channels, local space features and global space features are fused through deep convolution and global pooling, and the dynamic shuffling mechanism and the channel attention mechanism are combined. The generated future interaction features help the model to accurately simulate future vehicle behaviors.

The invention discloses a parking space distribution method and device, electronic equipment and a computer program product. The method comprises the steps that vehicle information of a target vehicle is acquired, and the vehicle information at least comprises a target vehicle identifier used for identifying the target vehicle; target parking behavior data corresponding to the target vehicle identifier is inquired in a historical database, and the target parking behavior data at least comprises historical parking time periods of the target vehicle; a vehicle behavior prediction model is adopted to analyze the target parking behavior data to obtain parking demand information of the target vehicle, and the parking demand information at least comprises a target parking time period of the target vehicle predicted by adopting the vehicle behavior prediction model; and distributing a target parking space matched with the target parking time period for the target vehicle according to pre-acquired parking space information of each parking space. The technical problem of low utilization efficiency of parking resources in the prior art is solved.

The invention relates to a method for operating an autonomously controlled vehicle (2), comprising at least one automated driving system (8) for controlling a vehicle journey, a vehicle interior (4) for a vehicle occupant (6), with at least one comfort system (10) for providing a comfort function for the vehicle occupant (6), and with at least one safety system (12) for protecting the vehicle occupant (6), an interior sensor (14) for detecting an interior situation (16) of the vehicle occupant (6), and an environment sensor (18) for detecting an environment situation (20), wherein a current actual driving situation is detected on the basis of system data (36) of the driving system (8), the comfort system (10) and / or the safety system (12) as well as sensor data (38) of the interior sensor (14) and the environment sensor (18).a key parameter (40) relating to the vehicle occupants (6) for a future driving situation is predicted and evaluated based on the system data (36) and the sensor data (38), based on the evaluation a vehicle behavior to realize a target driving situation is determined, the specific vehicle behavior is adapted taking into account an interaction between the interior situation (16) and / or the exterior situation (20), and the adapted vehicle behavior (42) is set by controlling the driving system (8), the comfort system (10) and / or the safety system (12).

Methods and systems are herein providing for predicting driving performance based on mental state. In one example, a vehicle system comprises a vehicle computing system comprising one or more processors and memory storing instructions that, when executed by the one or more processors, cause the vehicle computing system to adjust one or more vehicle behaviors according to predicted driving performance, wherein the predicted driving performance is determined based on one or more mental states of a driver.

The processor of ECU includes: a traffic light determination unit that determines whether or not a traffic light in front of the vehicle indicates that the vehicle can pass; an other-vehicle behavior determination unit that determines the behavior of the other vehicle existing in the vicinity of the vehicle; and a passableness determination unit that, when it is determined that the traffic light indicates that the vehicle can pass, determines whether or not the traffic light can pass by the vehicle based on the behavior of the other vehicle existing in the vicinity of the vehicle.

The invention discloses a non-motor vehicle violation and illegal behavior intelligent identification method based on multiple features, relates to the technical field of non-motor vehicle behavior identification, and solves the problem that in the prior art, non-motor vehicle violation behavior identification systems mostly depend on a single feature for identification, so that misjudgment or missed judgment is easily caused in a complex traffic scene. According to the method, the global semantic understanding capability of a large model is introduced, so that deep fusion of various characteristics such as appearance, movement, behaviors and environment is realized. Therefore, the system can understand the traffic scene more comprehensively, so that the traffic violation behavior of the non-motor vehicle can be identified more accurately. In addition, the method further improves the recognition capability of the key information through the attention mechanism and region enhanced feature extraction. The multi-feature fusion mode not only improves the recognition accuracy, but also enhances the robustness and generalization ability of the system, so that the system can better adapt to different traffic scenes and environmental conditions.

The invention discloses an expressway dynamic vehicle behavior trajectory tracking induction monitoring system, which relates to the technical field of intelligent traffic monitoring, and comprises a multi-source data acquisition module used for responding to a trigger signal of a vehicle entering a monitoring area, acquiring multi-source data, and sending the multi-source data to the monitoring area; the multi-source data comprises three-dimensional point cloud data, a thermal infrared image, a multi-view visible light image, vehicle speed and lane data and a license plate image; and the preprocessing and calibration module is used for registering and fusing the thermal infrared image and the visible light image. According to the expressway dynamic vehicle behavior trajectory tracking induction monitoring system provided by the invention, through multi-source sensor fusion and intelligent algorithm innovation, high-precision tracking and real-time monitoring of vehicle behavior trajectories are realized, road safety and traffic efficiency are effectively improved, and the expressway dynamic vehicle behavior trajectory tracking induction monitoring system is suitable for popularization and application. Multi-dimensional sensors such as a laser radar, an infrared thermal inductance instrument and a visible light camera are adopted for cooperative work, and the capability of capturing vehicle information in a weak light environment is enhanced.

A concept for behavior planning of a vehicle in a traffic scene is proposed that extends the planning capabilities of a rule-based planner so that the rule-based planner can also be used for behavior planning in more complex scenarios. A rule-based planner (50) is used which, on the basis of a scene representation (2) of the traffic scene (1), carries out a behavioral planning (6) which simulates at least one planning scenario from a planner-specific set of predetermined planning scenarios, According to the invention, a text-based neural network (30) is used to select the at least one planning scenario. For this purpose, at least one text query (3) describing the traffic scene is generated for the neural network (30) based on the scene representation (2), with which a behavioral recommendation (4) is requested that is supported by the planner-specific set of predefined planning scenarios. Based on the text query (3), the neural network (30) generates a text-based behavioral recommendation (4) that is assigned to at least one of the predefined planning scenarios. The behavioral planning (6) of the rule-based planner (50) is then based on the planning scenario thus selected.

The invention relates to the technical field of vehicles, in particular to an automatic driving behavior decision-making method and device of a vehicle, the vehicle and a storage medium, and the method comprises the steps: generating a plurality of automatic driving behavior decision-making sequences of the vehicle in a preset action space based on current state information, and carrying out the screening to obtain an effective candidate decision-making sequence set, generating an automatic driving candidate track corresponding to each effective candidate decision sequence based on the effective candidate decision sequence set by using a preset kinematic model; based on the environment prediction information, after safety evaluation is conducted on each automatic driving candidate track, the total cost of the safety candidate tracks is calculated through a multi-dimensional cost function, and the automatic driving behavior decision corresponding to the safety candidate track with the minimum total cost serves as the optimal automatic driving behavior decision of the vehicle. Therefore, the problems of single decision dimension, incapability of effectively combining a global path planning target to ensure that the vehicle behavior is consistent with the macroscopic driving route and the like in related technologies are solved.

The invention discloses a blind area risk defense control method based on adjacent vehicle behavior inference. The blind area risk defense control method comprises the steps that a known environment risk potential energy field reflecting current known traffic constraints is constructed; a composite risk potential energy field of the view angle of the adjacent vehicles is deduced by resolving a decision sequence of the adjacent vehicles in traffic interaction; fitting and optimizing a driving style weight coefficient in a cost function through historical observation data of an adjacent vehicle; according to the driving style weight coefficient and the actually measured motion state of the adjacent vehicle, calculating an actually predicted energy residual error, and according to the actually predicted energy residual error, inferring the position information of the blind area traffic participant under the own vehicle coordinate system; non-dynamic features are extracted, and a spatial gain mask is generated according to the non-dynamic features and the position information of the blind area traffic participants under the own vehicle coordinate system; and calculating a residual risk potential energy field according to the known environmental risk potential energy field and the composite risk potential energy field, and calculating a corrected residual risk potential energy field according to the residual risk potential energy field and the spatial gain mask so as to perform blind area risk defense control.

Techniques are provided which may be implemented using various methods and / or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external CV2X input to determine, send, receive and utilize data elements to determine inter-vehicle spacing, intersection priority, lane change behavior and spacing and other autonomous vehicle behavior.

The invention discloses an intelligent parking system and a vehicle parking management method, and relates to the technical field of intelligent traffic and Internet of Things, and the method comprises the steps: S1, collecting vehicle position signals and vehicle image features, and carrying out the data fusion, and obtaining the real-time coordinates of a vehicle with direction information; s2, constructing a continuous track chain based on the real-time coordinates of the vehicle, and recognizing abnormal behaviors of the vehicle based on a preset track feature threshold value; and S3, generating and executing a corresponding barrier gate control instruction according to the abnormal behavior of the vehicle. According to the method, geomagnetism and video sensing data are fused in a dynamic self-adaptive mode, a vehicle continuous track chain is constructed, abnormal behaviors such as vehicle retention and turning around can be accurately recognized, real-time linkage with barrier gate control is achieved, and therefore the vehicle smashing prevention safety, the accuracy of illegal behavior recognition and the real-time performance of system response are effectively improved.

The invention provides a safety analysis method and system for an automatic driving Transform model, a product and a vehicle, and relates to the technical field of automatic driving, and the method comprises the steps: systematically recognizing the specific safety risk of the Transform model in the whole life cycle of an automatic driving system, and constructing a structured safety demonstration model and verifiable safety demands on the basis; further embedding the model into a vehicle dynamic control link, and positioning an unsafe control action caused by abnormal output of the model by using a system theoretical analysis method, so as to design and deploy a safety relief mechanism adaptive to a Transform architecture in a targeted manner; meanwhile, a multi-level verification system covering the internal mechanism of the model and the vehicle behavior is established, an empirical evidence supporting safety demonstration is generated, and continuous performance monitoring and closed-loop optimization are implemented in the operation stage, so that the long-term safety, reliability and compliance of the automatic driving system in a complex open environment are effectively guaranteed.