Reinforcement Learning Aided Sequential Optimization for Unsignalized Intersection Management of Robot Traffic
Robert Bosch Centre for Cyber Physical Systems, Indian Institute of Science at Bengaluru（班加罗尔印度科学学院罗伯特·博世网络物理系统中心）；Department of Electrical Engineering, Indian Institute of Science at Bengaluru（班加罗尔印度科学学院电气工程系）
We consider the problem of optimal unsignalized intersection management, wherein we seek to obtain safe and optimal trajectories, for a set of robots that arrive randomly and continually. This problem involves repeatedly solving a mixed integer program (with robot acceleration trajectories as decision variables) with different parameters, for which the computation time using a naive optimization algorithm scales exponentially with the number of robots and lanes. Hence, such an approach is not suitable for real-time implementation. In this paper, we propose a solution framework that combines learning and sequential optimization. In particular, we propose an algorithm for learning a shared policy that given the traffic state information, determines the crossing order of the robots. Then, we optimize the trajectories of the robots sequentially according to that crossing order. This approach inherently guarantees safety at all times. We validate the performance of this approach using extensive simulations and compare our approach against $5$ different heuristics from the literature in $9$ different simulation settings. Our approach, on average, significantly outperforms the heuristics from the literature in various metrics like objective function, weighted average of crossing times and computation time. For example, in some scenarios, we have observed that our approach offers up to $150\%$ improvement in objective value over the first come first serve heuristic. Even on untrained scenarios, our approach shows a consistent improvement (in objective value) of more than $30\%$ over all heuristics under consideration. We also show through simulations that the computation time for our approach scales linearly with the number of robots (assuming all other factors are constant). Learnt policies are implemented on physical robots with slightly modified framework to address real-world challenges.
Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields
Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino（电子工程与电信学院，托斯卡纳理工学院）；Dipartimento di Automatica e Informatica, Politecnico di Torino（自动化与信息学院，托斯卡纳理工学院）
Reward-Driven Automated Curriculum Learning for Interaction-Aware Self-Driving at Unsignalized Intersections
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Risk-Aware Reinforcement Learning for Autonomous Driving: Improving Safety When Driving through Intersection
School of Automotive Studies, Tongji University（同济大学汽车学院）；Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（香港理工大学航空及航空工程系）
SIGN-MAPPO: State-Intent-Aware Graph Multi-Agent Reinforcement Learning for Cooperative Driving under Imperfect V2X
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
