High-speed ramp entrance confluence control method

Abstract

The invention discloses a high-speed ramp entrance confluence control method, relates to the technical field of intelligent traffic vehicle motion control, and solves the technical problems of low high-speed ramp entrance confluence efficiency and low safety. According to the technical scheme, the method is characterized in that a vehicle motion control equation is established through a control area and a confluence area of a high-speed ramp entrance confluence scene, a multi-agent Markov decision is constructed based on the vehicle motion control equation, a multi-agent Q-learning algorithm is constructed based on the multi-agent Markov decision, and according to the multi-agent Q-learning algorithm, an optimal trajectory of vehicle confluence is solved to realize vehicle confluence. The optimal path of vehicle passing is solved through the multi-agent Q-learning algorithm, and a reward function containing energy consumption, time delay and safety quantity is innovatively constructed, so that the optimal energy consumption, optimal passing and safe driving are achieved during vehicle driving.

Description

technical field [0001] The present disclosure relates to the technical field of motion control of intelligent transportation vehicles, in particular to a control method for merging at a high-speed ramp entrance. Background technique [0002] With the increasing population and car ownership in our country, traffic congestion is also becoming more and more serious. High-speed ramp entrance scene is a common traffic environment in urban traffic. At present, the low driving safety of ramp vehicles and excessive energy consumption are mostly caused by the serious queuing phenomenon of vehicles; ramp vehicles stop and wait in order to find a suitable gap to merge into the main road , resulting in low traffic efficiency. Therefore, effective merging control at the entrance of the high-speed ramp will greatly improve the efficiency and safety of vehicle traffic, and reduce energy consumption. [0003] In the prior art, the confluence control method based on the minimum value princ...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, the confluence control method based on the minimum value principle cannot achieve real-time performance, which is not conducive to driving safety; the optimal control method represented by the dynamic programming control algorithm is mainly numerical solution, and the computational complexity is high; in addition, Few existing studies consider the three optimization objectives of driving safety, energy saving, and high efficiency at the same time, and only solve a single problem

Images