5results about How to "Ease traffic congestion" patented technology

The application discloses a highway cooperative decision safety driving method, comprising the following steps: S1, acquiring vehicle end sensing information and road end sensing information and performing information fusion; S2, establishing a plane rectangular coordinate system, and establishing a speed potential field, a lane potential field, a road boundary potential field and a solid obstacle potential field on a road surface according to the fused sensing information; wherein the speed potential field enables a vehicle to travel at a set expected speed, the lane potential field enables the vehicle to travel in a current lane, the road boundary potential field enables the vehicle to travel away from a road boundary, and the solid obstacle potential field enables the vehicle to travel away from a solid obstacle; S3, a traveling vehicle is affected by a total potential field formed by superposition of the speed potential field, the lane potential field, the road boundary potential field and the solid obstacle potential field, and target accelerations of the vehicle in a driving direction and a transverse direction are obtained according to a potential field intensity gradient of the total potential field at a vehicle position in the driving direction and the transverse direction; and S4, the vehicle travels according to the target accelerations in the driving direction and the transverse direction.

This invention relates to the field of vehicle management, specifically to a data management system and method based on vehicle traffic planning. The system includes an image perception module, a data fusion module, a path guidance module, a vehicle tracking module, and a replanning module. The image perception module identifies vehicle boundaries, the data fusion module performs indoor vehicle positioning, the path guidance module plans pedestrian and vehicle routes, the vehicle tracking module establishes a kinematic model and labels matched vehicles, and the replanning module recalculates the merging path to minimize the estimated merging time or travel cost. This invention can supplement missing image details, making the identification of key features of vehicles and pedestrians clearer, improving target detection efficiency, reducing waiting time for pedestrians and vehicles, alleviating traffic congestion in complex road conditions, shortening vehicle pick-up time, improving road capacity, and achieving two-way dynamic matching and visualized path collaboration between pedestrians and vehicles.

The application discloses a kind of holographic traffic perception method and system based on wide-area radar, belong to vehicle traffic control technical field, for solving the technical problems of existing traffic perception system, it is difficult to provide real-time, holographic traffic information service for mass vehicle, not conducive to optimizing traffic flow space-time distribution, and relieving congestion and reducing accident rate.Method includes: the coordinate data uniform processing of initial tracking data and initial driving data about latitude and longitude offset, respectively corresponding to obtain radar tracking data and target driving data;Radar tracking data and target driving data are calculated about the trajectory topology of target reference system, respectively to obtain first state estimation information and second state estimation information;And generate minimum topology distance optimization model;Based on minimum topology distance optimization model, relative position of multi-vehicle target is distinguished and identified, to obtain vehicle holographic information;Realize holographic traffic perception to global vehicle target.

The invention discloses an urban traffic flow prediction and signal optimization method based on a space-time diagram neural network. The method specifically comprises the following steps: S1, constructing an urban road network topological graph; s2, multi-source heterogeneous data fusion and feature extraction; s3, establishing a space-time diagram neural network prediction model; s4, a signal optimization algorithm based on a prediction result; s5, model training and online strategy updating; s6, integration and real-time reasoning optimization are carried out; according to the method, through constructing topological graph representation of an urban road network, fusing multi-source heterogeneous traffic data and adopting a deep learning architecture combining a graph convolutional network and a time sequence prediction model, accurate short-term prediction of traffic flows of intersections and road sections is realized, and a dynamic signal optimization algorithm is designed based on a prediction result, so that the traffic flow prediction efficiency is improved. The adaptive adjustment of traffic signal timing is realized, so that the traffic efficiency of a road network is improved, and the vehicle delay is reduced.

This invention provides a road right-of-way detection method for autonomous vehicles, relating to the field of autonomous driving technology, comprising: Step S1: sensing and continuously updating the current environment through sensors to acquire detection information; Step S2: processing the detection information and acquiring measured road right-of-way and calculated road right-of-way; Step S3: predicting the trajectories of surrounding vehicles, using the predicted trajectory results as constraints, and obtaining predicted road right-of-way based on the calculated road right-of-way; Step S4: storing the road right-of-way information in the form of a database or file; Step S5: using an onboard communication system to transmit the vehicle's position and speed information to other vehicles, realizing the sharing and management of road right-of-way; Step S6: displaying the road right-of-way information to the user in a visual form. This invention can effectively improve the safety of autonomous vehicles, increase the driving efficiency of autonomous vehicles, and provide a foundation for achieving fully autonomous driving.