1132 results about "Traffic scene" patented technology

Processing circuitry may configure a system to implement a CAR-OnMap (“CAROM”) air framework for vehicle localization and traffic scene reconstruction using the aerial video of the traffic scene. Such a system may obtain aerial video of a traffic scene including vehicles that traverse the traffic scene and a satellite map image of the traffic scene as a distinct reference image. In such an example, processing circuitry may determine aerial image reference points within the aerial image which correspond to reference points in the satellite map image of the traffic scene. Processing circuitry may responsively generate calibrated images of the traffic scene from individual frames of the aerial video and determine unique keypoints on the vehicles in the traffic scene. In such an example, processing circuitry may track the vehicles across the individual frames of the aerial video utilizing the unique keypoints. Processing circuitry may output vehicle metrics for the vehicles.

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention discloses a unified framework for solving automatic driving track prediction and planning consistency based on a world model. According to the method, through cooperative work of the automatic driving domain controller and the vehicle-mounted sensing system, end-to-end joint optimization of track prediction and planning in a complex traffic scene is realized, time sequence dependence and interaction dynamics among intelligent agents are accurately captured, and the prediction capability and robustness of a model are remarkably improved. The method comprises the following specific steps: firstly, constructing a generative world model, and generating potential future state representation by utilizing a behavior conditional and backtracking expansion technology; secondly, in combination with global modeling and a local convolutional network, multi-scale features are extracted, adaptive fusion is carried out, and a multi-modal prediction trajectory is generated; then, a multi-target planning model is adopted to integrate various driving indexes, and a track with the minimum loss function is generated; finally, path planning parameters are dynamically optimized through real-time environment perception and decision feedback, and the problems of prediction uncertainty and planning consistency of the automatic driving track are effectively solved.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention discloses an intention recognition method based on cross attention and multi-scale uncertainty. The intention recognition method comprises the following steps: preprocessing multi-modal data; parallel multi-modal feature coding oriented to intention recognition; the invention relates to multi-scale uncertainty perception decoding. According to the method, a parallelized multi-modal feature extraction path is constructed, and a hierarchical fusion mechanism based on cross attention is designed, so that deep semantic alignment and complementary enhancement of four types of heterogeneous information including the posture, the motion track, the global scene and the local vision of a rider are realized; the problems of incomplete feature representation and insufficient cross-modal correlation modeling caused by dependence on a single information source or adoption of a shallow fusion strategy in a traditional method are solved, so that the accuracy and robustness of intention recognition in a complex traffic scene are remarkably improved. According to the method, a multi-scale uncertainty perception decoding framework is introduced, risk early warning or context auxiliary verification is carried out on a low-confidence identification result, and the reliability of an automatic driving system in a safety critical scene is improved.

The invention discloses an intelligent ship autonomous collision avoidance method based on COLREGs and a DDPG algorithm, and relates to the technical field of an intelligent ship technology and an autonomous collision avoidance algorithm, and the method comprises the steps: building an intelligent ship kinematics model based on the motion parameters of a ship in a north-east coordinate system; aIS, radar and visual information are fused, COLREGs rule constraints are embedded, an environment model is constructed, and a ship size safety threshold value is calculated. According to the method, COLREGs and a DDPG algorithm are combined, a high-precision environment model is constructed by utilizing multi-source sensing data, and an algorithm structure is optimized aiming at a typical collision avoidance scene, so that the intelligent ship can automatically identify the meeting situation type, determine the way-giving responsibility or the direct navigation obligation and generate an optimal collision avoidance path in a complex marine environment; compared with a traditional collision avoidance method depending on manual driving and fixed rules, the autonomous collision avoidance capability of the ship in dynamic and complex ocean traffic scenes is remarkably improved, and the collision risk caused by human factors is reduced.

The invention provides a vehicle lane changing planning method and system based on a graph neural network and multiple agents, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining a traffic scene graph structure with multiple agents; using a multi-agent reinforcement learning algorithm and a corresponding reward function to train the graph neural network fusion model, and updating parameters of the graph neural network fusion model by minimizing dominant function-based strategy gradient loss and value function loss to obtain a trained graph neural network fusion model; analyzing the traffic scene graph structure by using the trained graph neural network fusion model to obtain a multi-agent decision strategy; and performing decision-making prior fusion on the multi-agent decision-making strategy, performing dynamic interaction with the environment, obtaining a vehicle lane changing planning result, and completing the vehicle lane changing planning. According to the method, the problems of low multi-agent trajectory tracking precision and poor robustness in a complex scene are solved.

The invention relates to the technical field related to automatic driving, in particular to a trajectory prediction method based on adaptive interaction and dynamic intention, which comprises the following steps: firstly, constructing a heterogeneous interaction map, dividing a traffic scene into a vehicle grid, an environment grid and a non-driving area grid, and embedding multi-dimensional dynamic features; then dynamically adjusting a region of interest based on the behavior intention of the target vehicle, and extracting a high-correlation interaction subnet; modeling an interaction relationship by adopting a heterogeneous graph convolutional network, and processing the motion characteristics of the target vehicle and the neighbor vehicle through a sub-channel coding strategy; further realizing dynamic intention perception through a double-branch parallel attention architecture, and fusing macroscopic intention and dynamic intention information; and finally, iteratively generating a future trajectory prediction result based on a decoding architecture of a message passing mechanism. The method can effectively improve the long-term prediction performance in a lane changing scene, adaptively captures a dynamic interaction relationship, and improves the adaptability of a prediction system to the behavior intention change of a driver.

The invention discloses a real-time monitoring system based on highway traffic flow monitoring, and the system specifically comprises a video collection and self-calibration module which obtains calibration video data with an aligned visual angle; the edge consistency adaptive enhancement module outputs an enhanced video frame, an edge intensity graph and a noise risk graph, and constructs an edge confidence graph; the improved segmentation SAM module is used for carrying out traffic scene domain adaptation training on the improved segmentation SAM model by adopting Adapter to generate an initial multi-target segmentation mask; the deformation optimization module is used for calculating a corresponding boundary stability index; the traffic flow index extraction module is used for generating a corrected tracking result; the abnormal event candidate recognition module is used for generating an event confidence score for each traffic flow abnormal event candidate; and the abnormal event output module is used for outputting graded abnormal event alarm information. According to the method, the problems of boundary edge eating, missing detection and inter-frame drifting are remarkably reduced, and high-time-space-consistency segmentation is realized.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention discloses a vehicle trajectory planning method and device and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: obtaining vehicle state information of a target vehicle, a reference trajectory and environment perception data containing available traffic width, and performing trajectory optimization processing according to the vehicle state information, the reference trajectory and the environment perception data to obtain a trajectory optimization result of the reference trajectory; in the trajectory optimization processing process, taking a first passing cost required for minimizing a trajectory optimization result as a target; a passing width cost item in the first passing cost is a power function taking the residual passing width as an independent variable, and the passing width cost item is increased along with the reduction of the residual passing width; the remaining passing width is the difference between the available passing width and the vehicle width of the target vehicle. Therefore, in a narrow traffic scene, the traffic width cost item is increased in a super-linear manner, the constraint strength on the transverse position of the track is enhanced, the generation of a high-risk track excessively close to an obstacle is effectively avoided, the planning interruption frequency is reduced, and the trafficability of the vehicle in the narrow scene is remarkably improved.

The invention is oriented to the field of autonomous vehicle perception planning, and discloses an autonomous vehicle trajectory prediction method fusing a Mama backbone network and a graph attention mechanism. According to the method, vehicle track, state, road and environment information is acquired, a Mamba backbone network is utilized to extract time sequence characteristics of tracks of an automatic driving vehicle and surrounding vehicles, and meanwhile, a spatial interaction relationship between the surrounding vehicles and other objects in a traffic scene is modeled through a graph attention network. In the feature fusion layer, the time sequence and space features are pooled to form high-dimensional space-time coding features. And the fusion features are input into a decoder, the decoder uses a Mamba module as a core to decode and output the information of the model coding, and the predicted trajectory of the vehicles around the autonomous vehicle is obtained. The provided model has the advantages that the mapping relation between the historical track and the predicted track is accurately modeled through deep learning, the mode of combining a Mama backbone network and a graph attention mechanism is utilized, the model is helped to understand the influence of scene features on the future driving track of the vehicle, and accurate, efficient and scene-robust prediction is achieved.

The invention provides an anti-interference and multi-target positioning method for a vehicle-mounted millimeter-wave radar, and the method comprises the steps: constructing a third-order cumulant interference suppression module, carrying out the processing of a signal received by the vehicle-mounted millimeter-wave radar through a third-order cumulant algorithm, suppressing the mutual interference between radars, and generating a high-resolution distance-angle thermodynamic diagram; constructing a dual-channel adaptive edge detection module, dividing the generated thermodynamic diagram into an original channel and an edge detection channel, dynamically allocating weights based on local gradient intensity, and fusing and outputting an enhanced thermodynamic diagram; and constructing a target identification and positioning module, performing target detection and positioning on the enhanced thermodynamic diagram through an improved YOLOv8 neural network, and outputting distance and angle information of the target. According to the method, the problems of mutual interference between radars, difficulty in detection of weak and small targets, low separation precision of overlapped targets and the like in a complex traffic scene are solved by combining high-order statistical signal processing and deep learning technologies, and the robustness and the real-time performance of target positioning are improved.

The invention discloses an automatic driving test scene library construction method based on real traffic data, and relates to the technical field of automatic driving, and the method comprises the following steps: based on a selective sensor fusion framework, dynamically adjusting a fusion strategy of a multi-modal sensor according to a current driving environment, and obtaining corresponding scene elements; performing hierarchical classification on scene elements, constructing a risk assessment model, calculating a comprehensive risk score, and preliminarily dividing risk levels; constructing a rule-based classifier by adopting an association rule mining technology on the basis of results of hierarchical classification and preliminary risk grading, and carrying out risk grading on the scene to be evaluated; and the scene elements and the risk levels are stored in a structured manner, and an automatic driving scene library supporting multi-dimensional query is constructed. According to the method, the characteristics of the traffic scene can be captured more comprehensively, scene elements can be identified more accurately, the risk levels of the scene can be divided scientifically, and the scene library is constructed by combining the scene elements and the risk levels, so that the diversity and pertinence of the test scene are improved.

The invention provides an anthropomorphic automatic driving simulation test scene construction method. The method comprises the steps that all vehicles in a traffic scene are divided into two types of intelligent agents including a test vehicle and an environment vehicle, the traffic scene is modeled into a Markov decision process, the test vehicle is a vehicle controlled by an automatic driving algorithm, and the environment vehicle is a vehicle controlled by a GAIL-GRU driving strategy model; a Markov decision process is utilized to extract a driving track from the human driving data set, and an expert track data set is generated; and training a GAIL-GRU driving strategy model by using the expert track data set, realizing interaction between an environment vehicle and a test vehicle by using the trained GAIL-GRU driving strategy model, and constructing a simulation test scene. The automatic driving simulation test scene constructed by the invention can effectively expose decision defects of an automatic driving algorithm in a complex interaction situation, has good anthropomorphism and relatively high risk scene coverage, and provides support for a decision control simulation test of a high-level automatic driving vehicle.

The invention relates to a method and system for identifying a highway event by using a video large model, and the method comprises the steps: employing a three-stage processing architecture, firstly carrying out the real-time target detection and preliminary event judgment of a highway monitoring video stream through employing a YOLO algorithm, and generating an event candidate set; inputting the candidate events and the video clips thereof into a specially trained visual large model for deep semantic analysis and secondary reasoning; and finally, a reasoning result is rechecked through a rule engine, and false alarms are filtered by applying illusion suppression and a space-time association rule. According to the method, the real-time performance of traditional target detection and the deep reasoning capability of a visual large model are fused, so that the problems of high false alarm rate and high missing report rate of a traditional method are effectively solved, the accuracy and reliability of event identification in a complex traffic scene are remarkably improved, and meanwhile, the real-time processing capability of a system on multiple paths of high-definition video streams is ensured.

The invention relates to the technical field of low-altitude traffic management, and discloses a low-altitude traffic situation analysis method and system based on digital twinning, and the method comprises the steps: constructing a non-uniform adaptive quadtree spatial index structure; generating a space-time risk probability field, and calculating a conflict risk probability by adopting trajectory prediction of multi-model fusion; constructing a scene adaptive classification model, and dynamically adjusting a decision strategy according to traffic scene characteristics; collaborative decision optimization is realized, and a hierarchical decision structure and a passing right distribution algorithm based on an auction mechanism are adopted; establishing a digital twinborn situation analysis platform, mapping a low-altitude traffic state in real time and providing decision support; according to the method, through organic combination of technologies such as non-uniform adaptive quadtree spatial indexing, multi-model fusion trajectory prediction, space-time risk probability field calculation, hierarchical collaborative decision optimization and a digital twinborn situation analysis platform, accurate perception, analysis and prediction of low-altitude traffic are realized.

The invention discloses an intelligent driving scene understanding and decision-making method and system based on a multi-modal large language model, and relates to the technical field of intelligent driving scene understanding and decision-making, and the method comprises the steps: collecting the visual, radar, laser radar, Internet of Vehicles, voice and vehicle state data of the surrounding environment of a vehicle, and forming a multi-modal original input set; performing feature extraction and semantic coding on various data in the multi-modal original input set to generate semantic feature vectors of corresponding modals; unified space mapping is carried out on the semantic feature vectors through a cross-modal alignment mechanism, multi-modal fusion processing is carried out based on an alignment result, and comprehensive semantic representation is generated; analyzing a social interaction relationship in the traffic scene based on the comprehensive semantic representation, identifying action modes and behavior tendencies of surrounding traffic participants, and generating social intention description information; and generating a scene query request according to the comprehensive semantic representation, and matching related traffic rules and driving experience in a pre-constructed driving common knowledge base.

The invention provides a traffic scene multi-target detection method and system based on deep learning, and relates to the technical field of traffic, and the method comprises the steps: carrying out the semantic prior driven multi-scale feature extraction of a multi-frame image, and carrying out the point-by-point fusion; obtaining a motion field through optical flow estimation and feature similarity calculation, and executing motion compensation to obtain a moving target mask; obtaining a static target boundary by using boundary regression decoupling and geometric consistency constraint; and finally, moving and static target results are combined, and quadratic regression is executed based on consistency evaluation. The dynamic and static targets in the traffic scene can be effectively detected, the boundary regression precision is improved, and the false detection rate caused by shielding is reduced.

The invention discloses a radar and visual target association fusion tracking cross-frame integration method in a traffic scene, which comprises the following steps of: firstly, acquiring original data of a sensor and pre-processing to generate aligned radar and visual targets, establishing prior fusion under the condition that the targets are not associated through complementary sensor target characteristics, the method comprises the following steps: acquiring a priori fusion set as an anchor point, establishing cross-frame anchor point matching between the priori fusion set and a traffic target tracking track, decoupling an anchor point matching result to generate a local target global association result under cross-frame constraint, and finally realizing secondary fusion of a radar and a visual target under a one-to-one condition by using a fusion criterion. And based on a filtering method, filtering updating fusion of radar and vision is realized, multi-target tracking is completed, and a track of a traffic participation target and a filtering fusion result are output. According to the method, the influence of the problems of target loss, deviation and the like of a single sensor on the traffic target tracking track stability in a traffic scene is effectively reduced, and the capability of continuously and accurately sensing the traffic target is remarkably improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a road intersection perception and scene understanding method combining a deep learning algorithm and a large language model, and the method comprises the steps: obtaining traffic visual data; performing deep learning target detection based on the traffic visual data to obtain an object corresponding to the traffic visual data, and recording related dynamic information to form a prompt label; based on a traffic monitoring image and a prompt label of a certain view angle of the intersection, scene description of a corresponding view angle of the intersection is obtained through processing of a large language model; and based on the intersection single-view scene description and the prompt label, enhancing the chained thinking fine-tuning large language model to obtain the traffic scene understanding and semantic description of the whole intersection.

The invention discloses a traffic scene small target detection method based on an adaptive spatial aggregation pyramid, and belongs to the field of computer vision and small target detection, and the method comprises the steps: designing a multi-scale aggregation attention mechanism to enhance the texture, shape and context information of a small target for the problem of weak semantic information of the small target; aiming at the problem of insufficient multi-scale fusion of small targets, designing an adaptive space aggregation pyramid depth fusion multi-size feature map; a small target loss function NEIoU is constructed to solve the problem that small target detection is sensitive to position deviation, and the convergence speed is increased; and meanwhile, a Soft-NMS strategy is adopted to alleviate the problem that small targets are mistakenly suppressed due to IoU calculation deviation or dense arrangement. According to the traffic scene small target detection method based on the adaptive spatial aggregation pyramid, the precision of small target detection is remarkably improved by improving semantic richness, multi-scale fusion and positioning precision; meanwhile, deployment is easy, the operation speed is high, and the requirement for real-time detection is met.

The invention provides a passable area reasoning method and system based on a visual language model, and the method comprises the steps: obtaining the multi-modal data of a vehicle and the current position information of the vehicle; analyzing the multi-modal data, and determining visual features and traffic symbol features; performing spatial position coding on the visual object and the traffic symbol elements, and determining aerial view angle coordinate information; performing semantic alignment on the visual features and the traffic symbol features, and determining a shared embedding representation; constructing a traffic semantic map by fusing, sharing and embedding representation based on a graph neural network and bird's-eye view coordinate information of a visual object and a traffic symbol element; and according to the current position information of the vehicle, the traffic semantic map and a preset traffic rule, generating a bird's-eye view semantic map including a passable area, a no-pass area and a semantic association relationship. According to the method and the device, semantic alignment and consistency expression of visual perception and traffic symbol recognition are realized, and further feasible region reasoning of a complex traffic scene is realized.

The invention provides a collaborative decision-making method and device for multiple automatic driving vehicles, and the method comprises the steps: obtaining and analyzing a traffic scene, so as to obtain the scene features of the traffic scene; based on a first neural network and the scene feature, encoding the scene feature to obtain scene feature encoding information; generating a scene graph structure according to the motion information of the plurality of vehicles and the interaction relationship among the plurality of vehicles; based on a second neural network and the scene graph structure, determining spatial interaction information among the plurality of vehicles, the second neural network being a graph neural network GCN; and determining a cooperative driving strategy among the plurality of vehicles according to the scene feature coding information and the space interaction information. According to the scheme, the collaborative decision-making process of the multiple automatic driving automobiles is optimized.

The invention discloses a transmitting power determination method and device, electronic equipment, a medium and a product, and relates to the technical field of radars. The transmitting power determination method comprises the following steps: acquiring historical time sequence characteristic information and environmental point cloud data acquired by a radar sensor, and determining target point cloud data and traffic scene perception characteristics according to the environmental point cloud data; determining a motion mode of a traffic participant in the environmental point cloud data according to the traffic scene perception feature and the historical time sequence feature information; determining participant categories of traffic participants in the target point cloud data based on a preset target classification model and the motion mode, and constructing a radar scene model according to the traffic scene perception features, the historical time sequence feature information and the participant categories; and obtaining target scene data of the radar scene model, and determining the target transmitting power of the radar sensor through the preset power determination model and the target scene data, thereby realizing adaptive determination of the target transmitting power according to the traffic scene perception characteristics and historical time sequence characteristic information.

The invention provides a YOLOv8 traffic sign real-time detection method based on edge calculation optimization, and the method comprises the steps: collecting traffic scene image data in real time, carrying out the preprocessing of the collected image data, inputting an optimized YOLOv8 network model, carrying out the feature extraction, carrying out the processing of feature maps of different scales through an SE module and a bidirectional feature fusion strategy, and carrying out the detection of a traffic sign. Entering a detection head for target detection to obtain a detection result; an obtained detection result is transmitted to a central server or an automatic driving system in a structured data format through a low-delay communication protocol; in a central server or an automatic driving system, real-time statistics and trend analysis are carried out on detection results, and through deep optimization and deployment strategy improvement on a YOLOv8 model, many limitations in an edge calculation scene in the prior art are overcome. Specifically, a lightweight optimization strategy combining model pruning, quantitative processing and an efficient inference engine is provided, and the detection precision and robustness are improved in combination with an environment adaptive image preprocessing method.

The invention discloses an intelligent network connection automobile active safety teaching control method based on digital twinning. The method comprises the steps that S1, multi-source sensor data are collected and preprocessed to generate a driving state data set; s2, inputting the state data into a digital twin model to drive a traffic scene and outputting a synchronous state; s3, based on Dueling-DDQN, executing strategy learning to generate an active safety control instruction; s4, the instruction response effect is simulated and verified in the virtual environment; s5, collecting driver operation behaviors, inputting the improved MHA-BiLSTM model to extract time sequence features, and outputting behavior features; and S6, comparing the driving behavior with a standard instruction item by item, calculating an operation deviation and a response difference, and generating a personalized active safety teaching task. According to the invention, efficient comparison and teaching feedback of the driving behavior and the active control strategy can be realized, and the intelligent level of driving training is improved.

The invention discloses a traffic scene risk identification method and device considering dynamic and static information fusion, and a storage medium. The method comprises the steps of 1, obtaining a traffic scene high-precision map and a time sequence track of each vehicle; track points in the time sequence tracks are converted into a high-precision map coordinate system, lane matching is carried out, and a time sequence track high-precision map of each vehicle is obtained; 2, predicting vehicle positions, and extracting dynamic and static characteristics of each vehicle time sequence track; 3, fusing the dynamic and static features of each vehicle to obtain a fused feature; inputting the fusion features of all vehicles into a risk factor prediction model to predict and obtain dynamic and static risk factor prediction results; on the basis of prediction errors between dynamic and static risk factor prediction results and corresponding real results, combined with vehicle position prediction errors, calculating a comprehensive risk value of the traffic scene; the equipment and the storage medium are used for implementing the method. The method provides a new thought for traffic risk identification.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention relates to an intelligent driving control method of a vehicle and the vehicle, and belongs to the technical field of intelligent driving, and the method comprises the following steps: obtaining an original data stream of a vehicle sensor network, and carrying out feature extraction on a dynamic feature vector; state vectors of the traffic participants are extracted according to the dynamic feature vectors, edges between the two traffic participants are constructed, a traffic participant interaction graph is constructed, and an asymmetric factor matrix is calculated; acquiring a historical scene data set of the vehicle, calculating correction similarity, and calculating a weight coefficient; performing weighted fusion on the weight coefficient and the historical scene data set to obtain an enhanced data set; and performing updating training on the pre-trained intelligent driving decision model according to the enhanced data set to obtain an updated intelligent driving decision model, generating an intelligent driving decision, and controlling vehicle operation, thereby realizing more accurate characterization of a dynamic game relationship in a complex traffic scene, and improving the accuracy of the dynamic game relationship. And the intelligent driving decision model can continuously adapt to an asymmetric interaction mode effect in a real scene.