Intersection vehicle passing control method and system

Abstract

The invention discloses an intersection vehicle passing control method. The method in the scheme comprises the steps that a processor builds the constraint of the distance between each vehicle and an intersection based on the motion state information of each vehicle, the expected vehicle distance between each vehicle and a front vehicle corresponding to each vehicle, and the vehicle body length of the front vehicle corresponding to each vehicle; therefore, the running speed of each vehicle can be adjusted based on the actual passing sequence and the constraint of the distance between each vehicle and the intersection. According to the method, a constraint-oriented intersection passing model is innovatively established, a new control method is provided for the passing problem of an intelligent network connection vehicle at an intersection without signal lamps, and the distance from the vehicle to the intersection can be kept within a reasonable range through constraint on the vehicle; therefore, the safety that each vehicle runs according to the actual passing sequence by adjusting the running speed of the vehicle is realized. The invention further discloses an intersection vehicle passing control system which has the same beneficial effects as the intersection vehicle passing control method.

Description

technical field [0001] The invention relates to the field of unmanned driving and intelligent networked vehicles, in particular to a method and system for controlling vehicle traffic at an intersection. Background technique [0002] During the passage process of ICVs at non-signalized intersections, there is a problem of how to implement the macro-level traffic order decision-making to implement the merge control. The current solution is only around the macro-level traffic order decision-making. The default micro-control can be through PID (Proportion -Integral-Derivative, proportional integral differential control) and other engineering control methods to achieve. However, this method fails to fully consider factors such as the speed difference of vehicles coming from various directions, the distance between front and rear adjacent vehicles, and the length of the vehicle body in the actual scene. Execute the above-mentioned macro-traffic sequence decision accurately. Con...

AI Technical Summary

Problems solved by technology

However, this method fails to fully consider factors such as the speed difference of vehicles coming from various directions in the actual scene, the distance between adjacent vehicles, and the length of the vehicle body. Execute the above macro traffic sequence decision

Images