Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design

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.

[1]  Zohar Manna,et al.  Toward automatic program synthesis , 1971, Symposium on Semantics of Algorithmic Languages.

[2]  Yann LeCun,et al.  Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.

[3]  Dirk Haehnel,et al.  Junior: The Stanford entry in the Urban Challenge , 2008 .

[4]  Sanjiv Singh,et al.  The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA , 2009, The DARPA Urban Challenge.

[5]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[6]  Leslie Pack Kaelbling,et al.  Hierarchical Planning in the Now , 2010, Bridging the Gap Between Task and Motion Planning.

[7]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[8]  Wolfram Burgard,et al.  Learning driving styles for autonomous vehicles from demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Sergey Levine,et al.  Trust Region Policy Optimization , 2015, ICML.

[10]  Dan Klein,et al.  Neural Module Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Murray Shanahan,et al.  Towards Deep Symbolic Reinforcement Learning , 2016, ArXiv.

[12]  Stefano Ermon,et al.  Generative Adversarial Imitation Learning , 2016, NIPS.

[13]  Emilio Frazzoli,et al.  A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles , 2016, IEEE Transactions on Intelligent Vehicles.

[14]  Germán Ros,et al.  CARLA: An Open Urban Driving Simulator , 2017, CoRL.

[15]  Yee Whye Teh,et al.  The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables , 2016, ICLR.

[16]  Lihong Li,et al.  Neuro-Symbolic Program Synthesis , 2016, ICLR.

[17]  Amnon Shashua,et al.  On a Formal Model of Safe and Scalable Self-driving Cars , 2017, ArXiv.

[18]  Kai-Uwe Kühnberger,et al.  Neural-Symbolic Learning and Reasoning: A Survey and Interpretation , 2017, Neuro-Symbolic Artificial Intelligence.

[19]  Mykel J. Kochenderfer,et al.  Imitating driver behavior with generative adversarial networks , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[20]  Atul Prakash,et al.  Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[21]  Quoc V. Le,et al.  Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.

[22]  Xi Chen,et al.  Learning From Demonstration in the Wild , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[23]  Liang Lin,et al.  SNAS: Stochastic Neural Architecture Search , 2018, ICLR.

[24]  Jitendra Malik,et al.  Combining Optimal Control and Learning for Visual Navigation in Novel Environments , 2019, CoRL.

[25]  Yiming Yang,et al.  DARTS: Differentiable Architecture Search , 2018, ICLR.

[26]  Chuang Gan,et al.  The Neuro-Symbolic Concept Learner: Interpreting Scenes Words and Sentences from Natural Supervision , 2019, ICLR.

[27]  Tiberiu T. Cocias,et al.  A survey of deep learning techniques for autonomous driving , 2019, J. Field Robotics.

[28]  Bolei Zhou,et al.  Learning a Decision Module by Imitating Driver's Control Behaviors , 2019, CoRL.

[29]  DSNAS: Direct Neural Architecture Search Without Parameter Retraining , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  P. Alam ‘A’ , 2021, Composites Engineering: An A–Z Guide.