Signal Priority Control Of Multiple Emergency Vehicles Based On Deep Reinforcement Learning

The unexpected accidents in the city usually have a very big destructive effect on the normal order of urban traffic,and after the accident,if the emergency vehicle rescue is not timely,it will lead to greater personnel and economic losses.Therefore,it is very important for the emergency vehicle to arrive at the scene of the accident safely and quickly.Although the law gives the emergency vehicles absolute priority on the road,the impact of social vehicles on the emergency vehicles can not be ignored,especially in the traffic congestion section,it is difficult to guarantee the priority of emergency vehicles.Most of the urban traffic delays are caused by road intersections.The implementation of emergency vehicle priority control at intersections is one of the important means to ensure emergency rescue.However,the implementation of priority signal control at intersections can easily lead to the formation of blocking of non priority roads,Therefore,it is very necessary to ensure the safe and stable traffic flow in the road traffic network while ensuring the priority of emergency vehicles at the intersection.In this paper,a deep reinforcement learning algorithm is proposed to solve the problem that multiple emergency vehicles need priority signal control at the same time,The main contents are as follows:(1)Firstly,the existing emergency vehicle priority control strategy and bus priority control strategy are described in detail,and their shortcomings are analyzed.Combined with the emergency vehicle priority control theory,the feasibility of reinforcement learning,deep learning and deep reinforcement learning in the field of emergency vehicle signal priority control is analyzed.(2)Secondly,most of the researches on priority signal control of emergency vehicles at Isolated Intersections focus on emergency vehicles approaching the intersection from an entrance road,and then implement priority signal control of emergency vehicles.When multiple emergency vehicles seek priority signal at different entrance roads of the intersection,this method will not be applicable.To solve this problem,this paper models the priority control process of emergency vehicles as Markov process,codes the traffic state with mixed traffic of social vehicles and emergency vehicles,redefines the reward function,and realizes the priority control of multiple emergency vehicles by using the method based on deep double Q learning.Through sumo simulation results,the proposed method has achieved good results and strong robustness in low traffic period and high traffic period.(3)Finally,aiming at the deficiency of signal priority control based on route,by extending the priority signal control of emergency vehicles at isolated intersections to that of emergency vehicles at multiple intersections,a multi-agent deep reinforcement learning algorithm is proposed to solve the priority control problem of multiple emergency vehicles driving on the road at the same time.The simulation results show that compared with induction priority,induction priority has obvious advantages in maintaining the stability of the average speed of emergency vehicles and social vehicles.