A Calculation Method of State Vector of Intelligent Connected Vehicle Based on Vehicle-Road Information Coupling

Abstract

The invention provides a state vector calculation method for intelligent networked vehicles based on vehicle-road information coupling. The invention establishes a V2X-based multi-vehicle road segment scene, and designs a vehicle multi-lane spatiotemporal trajectory optimization method based on this. The method combines the current state information of the vehicle, the environmental information near the vehicle, and the signal timing information downstream of the road section, etc., to generate a V2X-based vehicle spatiotemporal trajectory. The method can optimize and generate real-time temporal and spatial trajectories of vehicles passing in a road section according to different road environments and traffic conditions, which increases the mutual cooperation ability between vehicles, improves the safety of passing road sections and the efficiency of vehicle traffic at intersections, and reduces the number of vehicles passing through the road. In order to ensure road traffic safety and improve travel efficiency, new solutions and theoretical basis are proposed.

Description

technical field [0001] The invention belongs to the technical field of vehicle-road coordination / arterial traffic flow control, and specifically relates to a method for calculating state vectors of intelligent networked vehicles based on vehicle-road information coupling, which is applicable to any signalized intersection section in an urban road traffic network. Background technique [0002] For the urban traffic road network, the current urban traffic road system controls the traffic flow at the intersection, except for a small number of suburban intersections with small traffic volumes that adopt the self-organizing control method without signals. The intersection is the main node connecting each road section, and the reasonable planning of traffic flow in each road section is also an important part of improving the traffic efficiency of the intersection. The driving behavior of the vehicle during the driving process of the road section can be divided into lane keeping be...

AI Technical Summary

Problems solved by technology

Compared with the single-lane space-time trajectory optimization problem, the multi-lane space-time trajectory optimization problem has higher complexity, and it is difficult to directly solve the multi-lane space-time trajectory optimization problem through the existing lane changing rules

[0013] 2. Traditional routing methods have limited information acquisition capabilities, often rely on fixed traffic detectors, and lack real-time, efficient, and accurate vehicle driving information and traffic status information

[0014] 3. Most of the traditional routing methods do not consider the factor of multi-vehicle coordinated lane change, and lack of consideration of the interference factors of surrounding vehicles

Establishing a mathematical model for the existing lane-changing rules is difficult to meet the multi-vehicle goal at the same time; for the mathematical calculation of the system, it is difficult to process a large amount of data of multiple vehicles at the same time

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