Can autonomous vehicles identify, recover from, and adapt to distribution shifts?

Jul 12, 2020



Out-of-distribution (OOD) driving scenarios are a common failure of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaption to OOD scenes can mitigate their adverse effects. However, no benchmark evaluating OOD detection and adaption currently exists to compare methods. In this paper, we introduce an autonomous car novel-scene benchmark, CARNOVEL, to evaluate the robustness of driving agents to a suite of tasks involving distribution shift. We also highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called robust imitative planning (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident but catastrophic extrapolations in out-of-training-distribution scenes. When the model's uncertainty quantification is insufficient to suggest a safe course of action by itself, it is used to query the driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term adaptive robust imitative planning (AdaRIP).



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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