SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving
CoRR(2023)
摘要
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
reduction in prediction collision rates. To facilitate future research, we
release our code to the public: github.com/cmubig/SafeShift
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关键词
robust trajectory
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