物理情報に基づく安全制御器でエンドツーエンド自動運転の計画を強化
Enhance Planning with Physics-informed Safety Controller for End-to-end Autonomous Driving
深層学習ベースの軌道計画にポテンシャル場とモデル予測制御を組み合わせ、訓練データ外の状況でも安全に走行できる融合フレームワークFusionAssuranceを提案し、CARLAで有効性を示した。
著者: Hang Zhou, Haichao Liu, Hongliang Lu, Dan Xu, Jun Ma, Yiding Ji
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Recent years have seen a growing research interest in applications of Deep Neural Networks (DNN) on autonomous vehicle technology. The trend started with perception and prediction a few years ago and it is gradually being applied to motion planning tasks. Despite the performance of networks improve over time, DNN planners inherit the natural drawbacks of Deep Learning. Learning-based planners have limitations in achieving perfect accuracy on the training dataset and network performance can be affected by out-of-distribution problem. In this paper, we propose FusionAssurance, a novel trajectory-based end-to-end driving fusion framework which combines physics-informed control for safety assurance. By incorporating Potential Field into Model Predictive Control, FusionAssurance is capable of navigating through scenarios that are not included in the training dataset and scenarios where neural network fail to generalize. The effectiveness of the approach is demonstrated by extensive experiments under various scenarios on the CARLA benchmark.
