1667 results about "Traffic scene" patented technology

The invention provides an intelligent networked automobile dynamics simulation method and system based on a physical engine, and relates to the technical field of intelligent networked automobiles, and the method comprises the steps: obtaining vehicle parameters and road condition information of an intelligent networked automobile, optimizing a multi-agent cooperative control model through a self-adaptive optimization algorithm based on the vehicle parameters and the road condition information, and carrying out the optimization of the multi-agent cooperative control model. Constructing a digital twinborn model, and mapping the optimized multi-agent cooperative control model to an actual intelligent networked automobile control system; the method comprises the following steps: establishing a distributed edge computing network of an intelligent networked automobile group, constructing a decentralized knowledge base by utilizing a block chain technology, forming a decision-making experience data set, designing a multi-level attention mechanism to extract target information based on the decision-making experience data set and multi-modal data of a current traffic scene, and generating a final vehicle control instruction. And transmitting the final vehicle control instruction to a vehicle execution system to realize dynamic adaptive group cooperative control of the intelligent networked vehicle.

The invention relates to the technical field of intelligent traffic prediction, in particular to a time-space correlation traffic flow prediction method based on deep learning, which comprises the following steps: S1, collecting traffic flow data and related external environment data in real time, and establishing an initial traffic network topology structure; s2, calculating the dynamic weight of each node; s3, dynamically adjusting the weight of each edge in the traffic network according to the node weight change in the S2 so as to update the topological structure of the traffic network; s4, performing feature extraction on the spatial dependency relationship among the traffic nodes, and extracting time evolution features of the traffic flow data at the same time; s5, constructing an adaptive space-time fusion model; s6, traffic flow prediction is carried out, and adaptive space-time fusion model parameters are optimized; according to the method, through dynamic fusion of the spatial-temporal characteristics and adaptive adjustment of the model parameters, the precision and real-time performance of traffic flow prediction are effectively improved, and the adaptability and generalization ability of the model in a complex dynamic traffic scene are enhanced.

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.

The invention relates to an automatic driving vehicle path planning method and device based on a large language model, equipment and a medium. The method comprises the steps that a traffic scene RGB image, a traffic scene depth image, laser radar point cloud data and inertial measurement unit data of an automatic driving vehicle are acquired; performing feature extraction on the traffic scene RGB image, the traffic scene depth image, the laser radar point cloud data and the inertial measurement unit data to construct multi-modal traffic scene information; calling a preset large language model to determine the motion state of the autonomous vehicle and natural language description of traffic targets around the autonomous vehicle according to the multi-mode traffic scene information; and determining the attention weight of the target node by adopting an attention mechanism in a large language model according to the natural language description of the traffic target, and determining the optimal driving path by adopting a preset path planning algorithm according to the attention weight of the target node, the motion state of the autonomous vehicle and the target position. According to the invention, the adaptive capability of path planning is obviously improved.

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.

Disclosed in the present invention are an autonomous driving control system and method based on complex network cognition and end-to-end federated reinforcement learning, and a vehicle device. An autonomous driving algorithm framework (FLDPPO) based on complex network cognition and end-to-end federated reinforcement learning under urban dense traffic is provided, and by designing a loss function, a combination of rule-based complex network cognition and end-to-end federated reinforcement learning is achieved. In the FLDPPO, dynamic driving suggestions are used to guide agents to learn rules, so that the agents can cope with a complex urban driving environment and a dense traffic scenario. Moreover, the provided framework uses a multi-agent federated reinforcement learning architecture, and models are trained by means of parameter aggregation, protecting the privacy of a vehicle end while accelerating network convergence, reducing communication consumption, and achieving the balance between sample efficiency and high robustness of the models.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

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, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention discloses a collision risk assessment method and system based on digital twinning and trajectory prediction, and the method comprises the steps: constructing a vehicle-road cooperation environment through a distributed digital twinning system, guaranteeing that vehicle information is always in a latest state through a prediction and updating system of a model, and providing a reliable data source for trajectory prediction; based on the multi-dimensional space-time interaction characteristics of vectorization processing, a multi-agent multi-mode parallel trajectory prediction algorithm is provided, and the motion states of multiple vehicles at the future moment are obtained at the same time; a driving risk field is dynamically generated according to multi-dimensional kinematics characteristics of a vehicle, and multi-source collision risks in a traffic scene are modeled in a three-dimensional mode by combining road characteristics and static obstacle characteristics. According to the method, the optimization method is integrated, fine-grained modeling is carried out on the traffic scene by using the digital twinborn technology, multi-agent trajectory prediction is combined with the field theory, and the timeliness and accuracy of collision risk assessment in the complex traffic scene are greatly improved.

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 load balancing control method based on data traffic, and relates to the technical field of data transmission. Comprising the following steps: constructing a flow monitoring module based on data flow, constructing a self-adaptive flow classification model, dynamically allocating weights to load nodes with balanced loads, dynamically adjusting a transmission path of the flow, carrying out real-time load balancing scheduling, and processing abnormal flow. According to the load balancing control method based on the data traffic, the self-adaptive traffic classification model and the multi-path traffic reconstruction algorithm are introduced, so that the defects in the prior art are fundamentally overcome. The real-time performance, the stability and the resource utilization rate of the system are remarkably improved through deep classification of the traffic characteristics and a multi-path dynamic segmentation strategy. According to the method, resource allocation can be optimized in a complex traffic scene, the load balancing efficiency is improved, meanwhile, the service quality of delay-sensitive and bandwidth-intensive services is guaranteed, and the availability and reliability of the whole system are enhanced.

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.

In various examples, a traffic model including one or more traffic scenarios may be generated and / or updated based on using human feedback. Human feedback may be provided indicating a preference for various traffic scenarios to identify which scenarios in a model are more realistic. A reward model may capture the preference information and rank the realism of one or more traffic scenarios.

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 roadside sensing unit optimization deployment method under partial vehicle-road cooperation conditions, and belongs to the technical field of roadside sensing unit optimization deployment, and the method comprises the steps: building traffic scenes, and obtaining a true value probability occupation grid of each traffic scene through multi-time-step continuous simulation; carrying out sensing parametric modeling on the roadside sensing unit and the intelligent networked automobile, establishing a ray tracing algorithm considering the shielding effect, and obtaining an observable probability occupation grid under a vehicle-road collaborative sensing condition through the ray tracing algorithm considering the shielding effect; a cross entropy of a true value probability occupancy grid and an observable probability occupancy grid is used as a traffic flow monitoring-oriented vehicle-road collaborative perception substitution evaluation index, and optimization deployment is carried out on a roadside perception unit; a specific complete technical framework is provided for introducing POG into the field of RPU optimization layout, and the problem that in the prior art, influences on ICV are insufficiently considered when RPU optimization layout modeling is carried out is solved.

The invention relates to a multi-modal trajectory prediction method based on vehicle interaction diagram space-time decoupling coding, and belongs to the technical field of automatic driving automobiles. The method comprises the following steps: generating graph structure data according to a traffic scene trajectory data set; constructing an interaction graph considering multiple traffic factors based on the graph structure data; when the characteristics of the interaction graph are updated, capturing the change of the node characteristics of the interaction graph on a time sequence through a time sequence coding module; multi-modal prediction is completed by constructing a trajectory decoding module and a fractional decoding module; and designing a prediction model loss objective function and carrying out training. According to the method, the influence of multi-source factors on motion prediction is combined through a heterogeneous graph attention method, decoupling coding is carried out on spatial-temporal characteristics, and the characteristics on a time sequence are better captured. Compared with a traditional vehicle track prediction method, the method can complete more refined prediction, and is better in scene generalization.

The invention provides an intelligent traffic management system and method based on cloud computing, and relates to the technical field of traffic management. The intelligent traffic management system based on cloud computing comprises a multi-source data acquisition module, the multi-source data acquisition module is connected with a data fusion and preprocessing module, and the data fusion and preprocessing module is connected with a behavior prediction module. Real-time data acquisition and a behavior prediction model are utilized, signal lamp time and shunting strategies can be flexibly adjusted, traffic condition changes can be coped with in real time, management efficiency is improved, interaction among all traffic participants is optimized through group behavior analysis, balance and high efficiency of the whole traffic flow are ensured, and the traffic safety is improved. Through reinforcement learning and an adaptive algorithm, the method can continuously learn and optimize a strategy from feedback, dynamically adapts to different cities and traffic scenes, provides personalized suggestions and guidance for each traffic participant through multiple channels, and improves travel experience and safety.

The invention relates to the technical field of highway management and control, in particular to a highway hard shoulder opening management and control method based on artificial intelligence. The scheme comprises the steps of collecting traffic flow and vehicle speed in real time; transmitting the preprocessed data to a central control system through a high-speed communication network; testing and optimizing the intelligent decision algorithm by using traffic simulation software, evaluating the performance of the algorithm by simulating different traffic scenes, and adjusting parameters according to a test result; testing and optimizing the optimal strategy by using traffic simulation software, and adjusting parameters according to a test result; integrating the optimized intelligent decision algorithm into a control system of the expressway; the performance of the expressway control system is continuously monitored, the performance comprises the passing efficiency and the accident rate, and the optimal strategy for opening the hard road shoulder is optimized online. According to the scheme, road side sensing is combined with automatic road shoulder development control dynamic adjustment, and safety and passing efficiency of a road system are achieved.

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 automatic driving target detection method based on improved RT-DETR, and relates to the technical field of target detection and automatic driving, and the method comprises the steps: obtaining a disclosed automatic driving target detection data set SODA10M; a backbone network of an FSSD-DETR algorithm is constructed, and a BaseBlock optimization feature extraction process in a P5 layer is replaced with an FDC module; a small target detection layer is introduced into the neck network of the RT-DETR algorithm to improve the detection performance of a remote small target; a neck network of an FSSD-DETR algorithm is constructed, and a Zoomcat module and a ScalSeq module are used to optimize a feature fusion process; an up-sampling operator of the FSSD-DETR algorithm is constructed, and a DySample up-sampling operator is used to replace a nearest neighbor interpolation method so as to improve the image quality; therefore, good performance is maintained when complex traffic scenes such as small targets, dense vehicles and shielding are processed.

The invention relates to reinforcement learning for traffic simulation. In various examples, a traffic model including one or more traffic scenes may be generated and / or updated based on the use of human feedback. Human feedback may be provided indicating preferences for various traffic scenes to determine which scenes in the model are more realistic. The reward model may capture preference information and rank the authenticity of one or more traffic scenes.

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.