都市環境での自動運転のための条件付きアフォーダンス学習
Conditional Affordance Learning for Driving in Urban Environments
動画入力から都市環境での自動運転に適した中間表現を学習し、方向指示に従って交差点・信号・速度制限に対応する直接知覚手法を提案。CARLAベンチマークで最大68%の改善を達成。
著者: Axel Sauer, Nikolay Savinov, Andreas Geiger
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limits. In this work, we propose a direct perception approach which maps video input to intermediate representations suitable for autonomous navigation in complex urban environments given high-level directional inputs. Compared to state-of-the-art reinforcement and conditional imitation learning approaches, we achieve an improvement of up to 68 % in goal-directed navigation on the challenging CARLA simulation benchmark. In addition, our approach is the first to handle traffic lights and speed signs by using image-level labels only, as well as smooth car-following, resulting in a significant reduction of traffic accidents in simulation.
