低レベル制御の模倣による自動運転の意思決定モジュール学習
Learning a Decision Module by Imitating Driver's Control Behaviors
古典的なモジュール型パイプラインの意思決定部を、人間の低レベル制御行動の模倣を通じて学習させるハイブリッド手法を提案し、複雑なシナリオでルールベースより優れ、端到端ポリシーより滑らかで安全な走行を実現した。
著者: Junning Huang, Sirui Xie, Jiankai Sun, Qiurui Ma, Chunxiao Liu, Jianping Shi, Dahua Lin, Bolei Zhou
分類: cs.AI, cs.RO, eess.SP
原文アブストラクト
Autonomous driving systems have a pipeline of perception, decision, planning, and control. The decision module processes information from the perception module and directs the execution of downstream planning and control modules. On the other hand, the recent success of deep learning suggests that this pipeline could be replaced by end-to-end neural control policies, however, safety cannot be well guaranteed for the data-driven neural networks. In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning. This hybrid framework can preserve the merits of the classical pipeline such as the strict enforcement of physical and logical constraints while learning complex driving decisions from data. To circumvent the ambiguous annotation of human driving decisions, our method learns high-level driving decisions by imitating low-level control behaviors. We show in the simulation experiments that our modular driving agent can generalize its driving decision and control to various complex scenarios where the rule-based programs fail. It can also generate smoother and safer driving trajectories than end-to-end neural policies.
