SafeShift: 自動運転における安全志向の分布シフトによる堅牢な軌道予測
SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving
既存の実世界データセットから潜在的な安全クリティカルなシナリオを抽出し、軌道予測モデルを安全志向の分布シフト下で評価する枠組みを提案。予測衝突率を平均10%削減する改善策も示した。
著者: Benjamin Stoler, Ingrid Navarro, Meghdeep Jana, Soonmin Hwang, Jonathan Francis, Jean Oh
分類: cs.RO
原文アブストラクト
As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Motion, provide realistic recorded scenarios for model development, they often lack truly safety-critical situations. Rather than utilizing unrealistic simulation or dangerous real-world testing, we instead propose a framework to characterize such datasets and find hidden safety-relevant scenarios within. Our approach expands the spectrum of safety-relevance, allowing us to study trajectory prediction models under a safety-informed, distribution shift setting. We contribute a generalized scenario characterization method, a novel scoring scheme to find subtly-avoided risky scenarios, and an evaluation of trajectory prediction models in this setting. We further contribute a remediation strategy, achieving a 10% average reduction in prediction collision rates. To facilitate future research, we release our code to the public: github.com/cmubig/SafeShift
