Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Angelos Filos, Panagiotis Tigkas, Rowan Mcallister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3145-3153, 2020.

Abstract

Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaptation to OOD scenes can mitigate their adverse effects. In this paper, we highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called \emph{robust imitative planning} (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. If the model’s uncertainty is too great to suggest a safe course of action, the model can instead query the expert driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term \emph{adaptive robust imitative planning} (AdaRIP). Our methods outperform current state-of-the-art approaches in the nuScenes \emph{prediction} challenge, but since no benchmark evaluating OOD detection and adaption currently exists to assess \emph{control}, we introduce an autonomous car novel-scene benchmark, \texttt{CARNOVEL}, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts, where our methods outperform all the baselines.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-filos20a, title = {Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?}, author = {Filos, Angelos and Tigkas, Panagiotis and Mcallister, Rowan and Rhinehart, Nicholas and Levine, Sergey and Gal, Yarin}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {3145--3153}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/filos20a/filos20a.pdf}, url = {http://proceedings.mlr.press/v119/filos20a.html}, abstract = {Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaptation to OOD scenes can mitigate their adverse effects. In this paper, we highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called \emph{robust imitative planning} (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. If the model’s uncertainty is too great to suggest a safe course of action, the model can instead query the expert driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term \emph{adaptive robust imitative planning} (AdaRIP). Our methods outperform current state-of-the-art approaches in the nuScenes \emph{prediction} challenge, but since no benchmark evaluating OOD detection and adaption currently exists to assess \emph{control}, we introduce an autonomous car novel-scene benchmark, \texttt{CARNOVEL}, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts, where our methods outperform all the baselines.} }
Endnote
%0 Conference Paper %T Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? %A Angelos Filos %A Panagiotis Tigkas %A Rowan Mcallister %A Nicholas Rhinehart %A Sergey Levine %A Yarin Gal %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-filos20a %I PMLR %P 3145--3153 %U http://proceedings.mlr.press/v119/filos20a.html %V 119 %X Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In principle, detection of and adaptation to OOD scenes can mitigate their adverse effects. In this paper, we highlight the limitations of current approaches to novel driving scenes and propose an epistemic uncertainty-aware planning method, called \emph{robust imitative planning} (RIP). Our method can detect and recover from some distribution shifts, reducing the overconfident and catastrophic extrapolations in OOD scenes. If the model’s uncertainty is too great to suggest a safe course of action, the model can instead query the expert driver for feedback, enabling sample-efficient online adaptation, a variant of our method we term \emph{adaptive robust imitative planning} (AdaRIP). Our methods outperform current state-of-the-art approaches in the nuScenes \emph{prediction} challenge, but since no benchmark evaluating OOD detection and adaption currently exists to assess \emph{control}, we introduce an autonomous car novel-scene benchmark, \texttt{CARNOVEL}, to evaluate the robustness of driving agents to a suite of tasks with distribution shifts, where our methods outperform all the baselines.
APA
Filos, A., Tigkas, P., Mcallister, R., Rhinehart, N., Levine, S. & Gal, Y.. (2020). Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:3145-3153 Available from http://proceedings.mlr.press/v119/filos20a.html.

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