Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design

Jiankai Sun, Hao Sun, Tian Han, Bolei Zhou
Proceedings of the 2020 Conference on Robot Learning, PMLR 155:21-30, 2021.

Abstract

As a promising topic in cognitive robotics, neuro-symbolic modeling integrates symbolic reasoning and neural representation altogether. However, previous neuro-symbolic models usually wire their structures and the connections manually, making the underlying parameters sub-optimal. In this work, we propose the Neuro-Symbolic Program Search (NSPS) to improve the autonomous driving system design. NSPS is a novel automated search method that synthesizes the Neuro-Symbolic Programs. It can produce robust and expressive Neuro-Symbolic Programs and automatically tune the hyper-parameters. We validate NSPS in the CARLA driving simulation environment. The resulting Neuro-Symbolic Decision Programs successfully handle multiple traffic scenarios. Compared with previous neural-network-based driving and rule-based methods, our neuro-symbolic driving pipeline achieves more stable and safer behaviors in complex driving scenarios while maintaining an interpretable symbolic decision-making process.

Cite this Paper

BibTeX
@InProceedings{pmlr-v155-sun21a, title = {Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design}, author = {Sun, Jiankai and Sun, Hao and Han, Tian and Zhou, Bolei}, booktitle = {Proceedings of the 2020 Conference on Robot Learning}, pages = {21--30}, year = {2021}, editor = {Kober, Jens and Ramos, Fabio and Tomlin, Claire}, volume = {155}, series = {Proceedings of Machine Learning Research}, month = {16--18 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v155/sun21a/sun21a.pdf}, url = {https://proceedings.mlr.press/v155/sun21a.html}, abstract = {As a promising topic in cognitive robotics, neuro-symbolic modeling integrates symbolic reasoning and neural representation altogether. However, previous neuro-symbolic models usually wire their structures and the connections manually, making the underlying parameters sub-optimal. In this work, we propose the Neuro-Symbolic Program Search (NSPS) to improve the autonomous driving system design. NSPS is a novel automated search method that synthesizes the Neuro-Symbolic Programs. It can produce robust and expressive Neuro-Symbolic Programs and automatically tune the hyper-parameters. We validate NSPS in the CARLA driving simulation environment. The resulting Neuro-Symbolic Decision Programs successfully handle multiple traffic scenarios. Compared with previous neural-network-based driving and rule-based methods, our neuro-symbolic driving pipeline achieves more stable and safer behaviors in complex driving scenarios while maintaining an interpretable symbolic decision-making process.} }
Endnote
%0 Conference Paper %T Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design %A Jiankai Sun %A Hao Sun %A Tian Han %A Bolei Zhou %B Proceedings of the 2020 Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2021 %E Jens Kober %E Fabio Ramos %E Claire Tomlin %F pmlr-v155-sun21a %I PMLR %P 21--30 %U https://proceedings.mlr.press/v155/sun21a.html %V 155 %X As a promising topic in cognitive robotics, neuro-symbolic modeling integrates symbolic reasoning and neural representation altogether. However, previous neuro-symbolic models usually wire their structures and the connections manually, making the underlying parameters sub-optimal. In this work, we propose the Neuro-Symbolic Program Search (NSPS) to improve the autonomous driving system design. NSPS is a novel automated search method that synthesizes the Neuro-Symbolic Programs. It can produce robust and expressive Neuro-Symbolic Programs and automatically tune the hyper-parameters. We validate NSPS in the CARLA driving simulation environment. The resulting Neuro-Symbolic Decision Programs successfully handle multiple traffic scenarios. Compared with previous neural-network-based driving and rule-based methods, our neuro-symbolic driving pipeline achieves more stable and safer behaviors in complex driving scenarios while maintaining an interpretable symbolic decision-making process.
APA
Sun, J., Sun, H., Han, T. & Zhou, B.. (2021). Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design. Proceedings of the 2020 Conference on Robot Learning, in Proceedings of Machine Learning Research 155:21-30 Available from https://proceedings.mlr.press/v155/sun21a.html.

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