Advisable learning for self-driving vehicles by internalizing observation-to-action rules

Jinkyu Kim, Suhong Moon, Anna Rohrbach, Trevor Darrell, John Canny

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

Humans learn to drive through both practice and theory, e.g. by studying the rules, while most self-driving systems are limited to the former. Being able to incorporate human knowledge of typical causal driving behaviour should benefit autonomous systems. We propose a new approach that learns vehicle control with the help of human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. “I see a pedestrian crossing, so I stop”), and predict the controls, accordingly. Moreover, to enhance interpretability of our system, we introduce a fine-grained attention mechanism which relies on semantic segmentation and object-centric RoI pooling. We show that our approach of training the autonomous system with human advice, grounded in a rich semantic representation, matches or outperforms prior work in terms of control prediction and explanation generation. Our approach also results in more interpretable visual explanations by visualizing object-centric attention maps. Code is available at https://github.com/JinkyuKimUCB/advisable-driving.

Original languageEnglish
Article number9157238
Pages (from-to)9658-9667
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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