1332 results about "Traffic scene" patented technology

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

In various examples, systems and methods are disclosed relating to generating scene mode conditioned trajectory predictions that are usable for interfacing with an LLM. A system can obtain traffic scene data associated with movement of one or more agents relative to a vehicle navigating through an environment. The system can encode the traffic scene data to determine latent representations of the movement of the one or more agents relative to the vehicle navigating through the environment. Then the system can determine a joint scene mode distribution based at least on the latent representations. The system can then decode the joint scene mode distribution into one or more trajectory predictions and one or more categorical predictions for each agent of the one or more agents.

The invention discloses a vehicle and road cloud integrated traffic control method for automatic driving lane changing, and the method comprises the following steps: S1, carrying out the multi-mode fusion of preprocessed multi-source sensor data, and generating environment state parameters; s2, on the basis of the environment state parameters, predicting a future traffic scene by using a traffic flow prediction model, performing risk assessment on a traffic flow prediction result, and generating a risk map containing spatio-temporal information; s3, according to the current traffic condition, the vehicle characteristics and the task requirements, self-adaptive role allocation is carried out on the vehicles running on the expressway, and a local cooperation network is established based on the roles allocated to the vehicles; s4, under the framework of the optimized traffic control strategy, processing the current traffic condition data and the traffic flow prediction result by using a distributed negotiation algorithm based on an auction mechanism, generating a plurality of candidate lane changing schemes, evaluating the candidate lane changing schemes, and determining an optimal lane changing strategy; and S5, according to the optimal lane changing strategy, coordinating each driving vehicle on the expressway to execute lane changing operation.

The invention discloses a traffic light intelligent adjustment method and system fused with Internet of Vehicles information, and relates to the technical field of intelligent traffic control, and the method comprises the steps: collecting multi-source traffic data through roadside equipment, and carrying out the time-space alignment and abnormal value filtering; constructing a deep learning prediction model based on the filtered data, and predicting a traffic demand in combination with historical and real-time Internet of Vehicles information; generating a dynamic timing strategy according to prediction, and synchronizing the dynamic timing strategy to an associated intersection through V2X; deploying an edge computing node to adjust traffic light parameters in real time, and sending a suggested speed to the vehicle; a closed-loop feedback mechanism is established, and strategies and models are corrected on line based on trajectory data and efficiency indexes. The technical problem that an existing traffic light timing system cannot fuse multi-source Internet of Vehicles data in real time to carry out accurate demand prediction and adaptive adjustment in a dynamic traffic environment is solved, and the technical effects of remarkably improving the traffic efficiency and shortening the emergency response delay in a complex traffic scene are achieved.

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 relates to the technical field of traffic scene target detection, in particular to a traffic scene target detection method based on feature fusion and an attention mechanism, and the method comprises the steps: obtaining a public traffic scene target image training data set; training a traffic target detection model by using the traffic scene target image training data set; and inputting a to-be-detected traffic scene image into the trained traffic scene target detection model to obtain an output detection result graph. A hierarchical receptive field is constructed through a feature fusion module, a fine structure of a target edge is reserved in a deep convolution stage, cross-channel semantic information is dynamically aggregated through point convolution, an attention module is put forward to dynamically adjust a convolutional receptive field and a space attention weight, interference of background noise is suppressed, and a target is obtained. The local detail feature expression of the target is better enhanced, a PIoU loss function is adopted, and the positioning precision and robustness are further improved by combining a target size adaptive penalty factor and an anchor frame quality-based gradient adjustment strategy.

The invention discloses a traffic flow prediction method based on multi-view fusion and diffusion diagram convolution. The traffic flow prediction method is suitable for dynamic modeling and space-time dependence extraction in a complex traffic scene. The method comprises the following steps: firstly, extracting short-term fluctuation and long-term periodic characteristics through double-path time slice convolution, and modeling multi-scale time dependence; in the aspect of spatial modeling, three types of graph structures including a static physical graph, a historical semantic graph and a current feature graph are fused, a sparse dynamic graph is generated through a Top-K mechanism, multi-order diffusion graph convolution is executed in combination with a static graph, and local and global spatial features are extracted. Furthermore, a space-time fusion strategy of bidirectional cross gating is provided, the information circulation direction between short-term and long-term features is dynamically adjusted, and coordinated fusion of multi-scale features is realized. And finally, the traffic state of multiple time steps in the future is output through a gating structure and a non-regression prediction module. According to the method, the modeling capability of the model for the complex traffic dependency relationship is effectively enhanced, and the prediction precision and robustness are remarkably improved.

The invention discloses a key safety scene generation system for an automatic driving automobile based on a diffusion model. The key safety scene generation system is characterized in that a traffic module performs de-noising processing on a noise track through the diffusion model and generates real diversified traffic scenes; the key safety scene generation module calls a mixed guidance function in each step of denoising process of the diffusion model to guide an opponent agent to collide with a self-agent so as to generate an antagonistic key safety scene; the expert trajectory optimization module performs multiple iterative optimization on the target function through an Adam optimizer to generate a safe and real expert trajectory; the planner enhancement module carries out fine adjustment on a pre-trained planner through the generated key safety scene and the expert track thereof, so that the ability of the planner to cope with extreme scenes is improved; the safety and robustness of the automatic driving planner in an extreme environment are improved by generating a real, diversified and antagonistic key safety scene.

The invention discloses a multi-vehicle cooperative driving control method and system for unmanned vehicles, and relates to the technical field of intelligent driving control, and the method comprises the steps: carrying out the multi-dimensional sensing of a plurality of unmanned vehicles, and obtaining a multi-dimensional sensing data set; environment modeling is carried out, and a dynamic traffic scene model is constructed; multi-vehicle path conflict analysis is carried out through the multi-vehicle cooperation platform, and a first path combination is generated; optimizing the first path combination according to the plurality of single-trajectory tracking results to generate a second path combination; and performing multi-vehicle intelligent cooperative driving control on the unmanned vehicle based on the multi-vehicle cooperative strategy. The technical problems that in the prior art, the environment sensing range of a single vehicle is limited, communication between vehicles is insufficient, complex and changeable traffic scenes are difficult to deal with, and consequently the multi-vehicle cooperative driving efficiency and safety are poor are solved, intelligent control over the unmanned vehicle in the dynamic traffic scene is achieved, and the driving safety is improved. The technical effect of improving the multi-vehicle cooperative driving efficiency and the driving safety is achieved.

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 a traffic flow prediction method based on a clustering and space-time fusion graph convolutional network, and belongs to the technical field of intelligent traffic. The method comprises the following steps: firstly, acquiring traffic flow data and resampling, and constructing microscopic and macroscopic traffic flow data sets; then constructing a space-time fusion graph convolutional neural network, wherein the space-time fusion graph convolutional neural network comprises a space-time fusion module and a macro-micro interaction module; training the model by using the data set; and finally, traffic flow prediction is carried out by using the trained model. Compared with the prior art, through a clustering algorithm and a unique network structure, the traffic flow time-space information is effectively integrated, the prediction precision is improved, the adaptability of the model to a complex traffic scene is enhanced, more reliable prediction support is provided for an intelligent traffic system, and optimization of traffic management and alleviation of congestion are facilitated.

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 relates to the technical field of dynamic data processing, and discloses an organic data reporting method, which comprises the following steps of: constructing a dynamic characteristic weight model, comprehensively quantifying the importance of data through time characteristics, spatial characteristics and context relevance, introducing differential privacy noise protection to high-weight data by utilizing a hierarchical privacy protection strategy, and reporting the data to the dynamic characteristic weight model; the low-weight data is subjected to lightweight encryption processing; a multi-level cache queue and a dynamic bandwidth allocation mechanism are combined, and low-delay reporting of high-priority data is preferentially guaranteed; and recalculating and comparing the dynamic characteristics of the data after privacy protection through a collaborative verification mechanism to ensure the context consistency of the data. According to the invention, the balance of privacy protection, security and real-time performance is realized through cooperation of multiple modules, the contradiction between privacy protection and data integrity is broken through, the transmission reliability and efficiency of high-priority data are remarkably improved in a high-traffic scene, and full-link optimization from data acquisition to data reporting is formed.

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 a traffic scene risk prediction method based on knowledge graph and multi-source knowledge fusion. The method comprises the following steps: S1, establishing a road traffic scene ontology reference model under the constraint of road design specifications; s2, acquiring a historical accident report, and extracting according to the historical accident report to obtain one or more first information groups; s3, after traversal of all historical accident reports is completed, risk values are calculated, multiple risk levels are obtained through clustering, risk nodes are generated according to the obtained risk levels, and a second information group is obtained through extraction; s4, acquiring a scene with an unknown risk level as a scene node to be reasoned, and constructing to obtain a security risk knowledge graph; s5, based on the security risk knowledge graph, aligning and fusing external risk knowledge to realize dynamic updating of the security risk knowledge graph; and S6, acquiring a to-be-assessed scene, matching the to-be-assessed scene with each scene node in the security risk knowledge graph, and outputting a risk level of the scene node obtained by matching as a risk detection result. Compared with the prior art, the method has the advantages of wide application range and the like.

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.

In a traffic scene, a traditional target detection algorithm always faces the problems of strong background interference, difficulty in small target detection and the like, and detection precision and robustness are affected. Therefore, the invention provides an improved YOLOv12n vehicle detection method in which an EMA (Empirical Multi-scale Attention) attention mechanism and an SFA (Space Feature Aggregation) attention mechanism are fused. The invention further provides a method for detecting the YOLOv12n vehicle based on the improved YOLOv12n vehicle based on the attention mechanism of the EMA (Empirical Multi-scale Attention) and the attention mechanism of the SFA (Space Feature Aggregation). The SFA module is deployed in a shallow network, key target area expression is enhanced by aggregating spatial features, and background noise interference is suppressed; the EMA module is embedded into a neck network, and the global information capture and multi-scale sensing capabilities are improved by adopting multi-scale convolution, cross-space modeling and feature grouping mechanisms. According to the method, the real-time performance is kept, meanwhile, the detection precision in a complex scene is remarkably improved, and particularly, higher robustness is shown in the aspects of small target recognition and shielding processing.

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 relates to a multi-source sensing fusion and dynamic resource allocation method based on vehicle infrastructure cooperation, and belongs to the technical field of mobile communication. The single-vehicle environment sensing is to complete the detection of the surrounding environment by using the sensing data of the single-vehicle environment sensing. However, the problem that heterogeneous data fusion efficiency is low due to the fact that the sensing range of single-vehicle sensing is limited exists. In order to solve the problem, the method comprises the following steps: firstly, constructing a vehicle-road double-layer cooperative sensing network, and improving the accuracy of shelter detection through a multi-source sensor space-time alignment and confidence weighting mechanism; secondly, designing an improved discrete dogvessel swarm optimization algorithm, balancing global exploration and local development by using a chain collaborative search strategy, and improving the perception fusion quality; and finally, developing a mixed action space depth deterministic strategy gradient algorithm, dynamically coordinating the resources of the intelligent network connection vehicle and the road side unit, and reducing the sensing time delay. The scheme is suitable for environmental perception optimization and edge computing resource scheduling of the vehicle in a complex traffic scene, and aims to improve perception fusion efficiency.

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 discloses an elastic resource allocation and barrier gate linkage control system and method for mixed traffic of parking lots, a medium and equipment, and the system comprises a vehicle type recognition module, a dynamic resource allocation module, a cooperative control decision module, an emergency channel management module and a multi-mode communication module. According to the parking lot mixed traffic management system integrating vehicle-road cooperative communication, double-layer game optimization and elastic resource pool division, an independent resource pool and a dynamic quota adjustment mechanism of automatic driving vehicles and manual driving vehicles are constructed, and a charging pile-barrier gate cooperative optimization unit and a multi-stage emergency response protocol are combined; the global optimization of parking lot resource allocation and traffic control is realized, and the problems of prominent resource competition contradiction, separation of barrier gate control and resource allocation, insufficient intelligence of an emergency response mechanism and short delay of multi-mode communication and decision cooperation in a mixed traffic scene are solved.

The invention discloses a traffic scene image data analysis and target segmentation method and device, and the method comprises the steps: enabling an input image of a traffic scene to obtain a target segmentation result through a traffic scene object segmentation model Tramba; the traffic scene object segmentation model Tramba comprises an S-stage visual state space encoder, S-1 double-frequency-domain visual state space modules, S-1-stage spiral visual state space decoders, an up-sampling module and a segmentation head. And the S-1 double-frequency-domain visual state space modules are used for performing high-frequency and low-frequency frequency domain enhancement on the RGB features of the first S-1 layers through a shift window scanning mechanism and an expansion scanning mechanism respectively and then connecting the RGB features to the input end of the spiral visual state space decoder of the corresponding level in a jumping manner. The invention aims to solve the problems that perception of a scanning mechanism in an existing Mama model on image details and a global structure needs to be enhanced and deep understanding on a traffic scene is lacked.