安全性を強化した都市部自動運転のための深層模倣学習
Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety
オフラインの走行データから都市部の運転方策を模倣学習で獲得し、テスト時には安全コントローラを組み合わせて安全性を保証する枠組みを提案した。
著者: Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka
分類: cs.RO
原文アブストラクト
The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.
