Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design
Jiankai Sun, Hao Sun, Tian Han, Bolei Zhou
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.
BibTeX
@inproceedings{corl2020_neurosymbolicpro,
title = {Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design},
author = {Jiankai Sun and Hao Sun and Tian Han and Bolei Zhou},
booktitle = {CoRL 2020},
year = {2020}
}