自動運転車の効率的な経路探索には都市と機械学習コミュニティの協力が不可欠
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
自動運転車の経路最適化が交通網を不安定にしうる問題をシミュレーションで示し、都市当局と機械学習コミュニティが協力して公平で効率的な経路アルゴリズムと規制基準を評価・監視すべきだと主張する位置論文。
著者: Anastasia Psarou, Ahmet Onur Akman, Łukasz Gorczyca, Michał Hoffmann, Grzegorz Jamróz, Rafał Kucharski
分類: cs.MA, cs.LG, cs.RO
原文アブストラクト
Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, with human drivers potentially experiencing longer travel times. We study this interaction by simulating human drivers and AVs. Our experiments with standard MARL algorithms reveal that, both in simplified and complex networks, policies often fail to converge to an optimal solution or require long training periods. This problem is amplified by the fact that we cannot rely entirely on simulated training, as there are no accurate models of human routing behavior. At the same time, real-world training in cities risks destabilizing urban traffic systems, increasing externalities, such as $CO_2$ emissions, and introducing non-stationarity as human drivers will adapt unpredictably to AV behaviors. In this position paper, we argue that city authorities must collaborate with the ML community to monitor and critically evaluate the routing algorithms proposed by car companies toward fair and system-efficient routing algorithms and regulatory standards.