摘要
Complex road conditions without signalized intersections when the traffic flow is nearly saturated result in high traffic congestion and accidents,reducing the traffic efficiency of intelligent vehicles.The complex road traffic environment of smart vehicles and other vehicles frequently experiences conflicting start and stop motion.The fine-grained scheduling of autonomous vehicles(AVs)at non-signalized intersections,which is a promising technique for exploring optimal driving paths for both assisted driving nowadays and driverless cars in the near future,has attracted significant attention owing to its high potential for improving road safety and traffic efficiency.Fine-grained scheduling primarily focuses on signalized intersection scenarios,as applying it directly to non-signalized intersections is challenging because each AV can move freely without traffic signal control.This may cause frequent driving collisions and low road traffic efficiency.Therefore,this study proposes a novel algorithm to address this issue.Our work focuses on the fine-grained scheduling of automated vehicles at non-signal intersections via dual reinforced training(FS-DRL).For FS-DRL,we first use a grid to describe the non-signalized intersection and propose a convolutional neural network(CNN)-based fast decision model that can rapidly yield a coarse-grained scheduling decision for each AV in a distributed manner.We then load these coarse-grained scheduling decisions onto a deep Q-learning network(DQN)for further evaluation.We use an adaptive learning rate to maximize the reward function and employ parameterεto tradeoff the fast speed of coarse-grained scheduling in the CNN and optimal fine-grained scheduling in the DQN.In addition,we prove that using this adaptive learning rate leads to a converged loss rate with an extremely small number of training loops.The simulation results show that compared with Dijkstra,RNN,and ant colony-based scheduling,FS-DRL yields a high accuracy of 96.5%on the sample,with improved performance of approximately 61.54%-85.37%in terms of the average conflict and traffic efficiency.
基金
Supported by National Natural Science Foundation of China(Grant No.61803206)
Jiangsu Provincial Natural Science Foundation(Grant No.222300420468)
Jiangsu Provincial key R&D Program(Grant No.BE2017008-2).
作者简介
Ning Sun is currently a lecturer at the College of Automobile and Traffic Engineering,Nanjing Forestry University,China.She received the Ph.D.degree in vehicle engineering from Southeast University,China;Weihao Wu has received his B.S.degree in automotive engineering and technology from Nanjing Forestry University,China,in 2020.Now he is working toward his master’s degree with the School of Automobile and Traffic Engineering,Nanjing Forestry University,China.His research interests include autonomous driving,route scheduling at non-signalized intersections and deep learning;Guangbing Xiao is an associate professor at the College of Automobile and Traffic Engineering,Nanjing Forestry University,China.He received his PhD degree and master degree both in computer science from University of Otago,New Zealand,in 2019,and Nanjing University of Information Science and Technology,China,respectively.His research interests include vehicular networks,smart driving,scheduling of platooning at non-signalized intersections,and decentralized network resource allocation;Correspondence:Guodong Yin born in 1976,received the Ph.D.degree in vehicle engineering from Southeast University,China,in 2007.From 2011 to 2012,he was a Visiting Research Scholar with the Department of Mechanical and Aerospace Engineering,Ohio State University,Columbus,OH,USA.He is currently a Professor with the School of Mechanical Engineering,Southeast University,China.His current research interests include vehicle dynamics and control,connected vehicles,and multiagent control.ygd@seu.edu.cn。
