無信号交差点における連結自動運転車のための密度考慮型連合模倣学習
Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection
プライバシー保護のため生データ共有が難しい複数事業者環境で、連合学習と模倣学習を組み合わせて無信号交差点の自動運転車制御を改善する手法を提案した。
著者: Tianhao Wu, Mingzhi Jiang, Yinhui Han, Zheng Yuan, Lin Zhang
分類: cs.MA, cs.RO
原文アブストラクト
Intelligent Transportation System (ITS) has become one of the essential components in Industry 4.0. As one of the critical indicators of ITS, efficiency has attracted wide attention from researchers. However, the next generation of urban traffic carried by multiple transport service providers may prohibit the raw data interaction among multiple regions for privacy reasons, easily ignored in the existing research. This paper puts forward a federated learning-based vehicle control framework to solve the above problem, including interactors, trainers, and an aggregator. In addition, the density-aware model aggregation method is utilized in this framework to improve vehicle control. What is more, to promote the performance of the end-to-end learning algorithm in the safety aspect, this paper proposes an imitation learning algorithm, which can obtain collision avoidance capabilities from a set of collision avoidance rules. Furthermore, a loss-aware experience selection strategy is also explored, reducing the communication overhead between the interactors and the trainers via extra computing. Finally, the experiment results demonstrate that the proposed imitation learning algorithm obtains the ability to avoid collisions and reduces discomfort by 55.71%. Besides, density-aware model aggregation can further reduce discomfort by 41.37%, and the experience selection scheme can reduce the communication overhead by 12.80% while ensuring model convergence.
