強化学習による無信号交差点での混在交通行動の分析
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections
強化学習で制御されたロボット車両と人間運転車両が混在する無信号交差点の交通を分析し、停止発進時の多峰性や車列長・流出量の相関、因果関係を調査した。
著者: Supriya Sarker
分類: cs.RO, cs.CY, cs.LG
原文アブストラクト
In this report, we delve into two critical research inquiries. Firstly, we explore the extent to which Reinforcement Learning (RL) agents exhibit multimodal distributions in the context of stop-and-go traffic scenarios. Secondly, we investigate how RL-controlled Robot Vehicles (RVs) effectively navigate their direction and coordinate with other vehicles in complex traffic environments. Our analysis encompasses an examination of multimodality within queue length, outflow, and platoon size distributions for both Robot and Human-driven Vehicles (HVs). Additionally, we assess the Pearson coefficient correlation, shedding light on relationships between queue length and outflow, considering both identical and differing travel directions. Furthermore, we delve into causal inference models, shedding light on the factors influencing queue length across scenarios involving varying travel directions. Through these investigations, this report contributes valuable insights into the behaviors of mixed traffic (RVs and HVs) in traffic management and coordination.
