Multi-target fusion intersection dynamic vehicle networking speed guide control method

Abstract

The invention discloses a multi-target fusion intersection dynamic vehicle networking speed guide control method, which comprises the following steps of: setting a reasonable initial vehicle prediction range according to vehicle and road information, and formulating a target function under multi-target fusion in a vehicle running process; adopting a rolling time domain model predictive control method, performing dynamic motorcade division on all automatic driving vehicles in an intelligent network connection environment, optimizing the operation efficiency of the motorcade, and guaranteeing the operation safety and stability of the motorcade ; and solving an optimization result of the control variable and the state variable of the vehicle after multi-target fusion by adopting a quadratic programming solving method, and providing a dynamic speed suggestion for the operation of the vehicle. According to the invention, the defects of the existing vehicle networking speed guide control method can be effectively made up.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and in particular relates to a dynamic vehicle network connection speed guidance control method at an intersection. Background technique [0002] Vehicle speed has positive significance for improving road traffic capacity, meeting the increasing demand for intercity traffic, and alleviating traffic pressure. Although most sections of urban highways have a maximum speed limit, highways are a dynamic system that includes people, vehicles, roads, and the environment. The optimal speed of vehicles may change dynamically under different conditions. [0003] With the development of intelligent network technology, more and more networked vehicles are gradually appearing on the actual road. In a networked environment, the computer can better track the driving status of the vehicle, and analyze the speed and safe driving of the vehicle. Therefore, how to accurately propose real-time and dynamic...

AI Technical Summary

Problems solved by technology

[0008] 1. The traditional vehicle network speed guidance control method based on 85% vehicle speed does not consider the impact of real-time dynamic changes of each vehicle in the fleet on the total road traffic;

[0009] 2. In the intelligent network environment, the vehicle speed control method considering the variable speed limit may not be able to meet the efficiency requirements of vehicle operation;

[0010] 3. Most of the current vehicle speed control algorithms only control a single vehicle on the road, rather than considering the global optimal solution of the overall speed of the fleet;

[0011] 4. The goal of the vehicle guidance control algorithm is often single, and it is impossible to simultaneously consider factors such as vehicle efficiency, emissions, fuel consumption, and driver comfort to obtain a comprehensive and optimal fleet speed guidance control algorithm

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