自動運転車のための堅牢なAI運転戦略
Robust AI Driving Strategy for Autonomous Vehicles
強化学習と制御バリア関数を組み合わせ、高速道路での自動運転において安全に学習・適応できる堅牢なAI運転戦略を提案する。
著者: Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic, Mrdjan Jankovic, Eric Tseng, Michael Hafner, Dimitar Filev
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the long tail distribution of human driver behavior, it has remained an open challenge for an intelligent vehicle to always know how to make and execute the best decision on road given available sensing / perception / localization information. In this chapter, we talk about how artificial intelligence and more specifically, reinforcement learning, can take advantage of operational knowledge and safety reflex to make strategical and tactical decisions. We discuss some challenging problems related to the robustness of reinforcement learning solutions and their implications to the practical design of driving strategies for autonomous vehicles. We focus on automated driving on highway and the integration of reinforcement learning, vehicle motion control, and control barrier function, leading to a robust AI driving strategy that can learn and adapt safely.
