Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

TL;DR

This paper introduces PAIN, a physically adversarial network for self-driving cars in simulation, which improves safety and robustness by training agents through aggressive interactions that enhance collision avoidance capabilities.

Contribution

We propose PAIN, a novel adversarial training framework using dual agents in simulation to improve self-driving vehicle safety and robustness against edge cases.

Findings

Protagonist agents trained with PAIN show increased resilience to environmental uncertainties.

PAIN-trained agents have higher mean-time-to-failure and travel further without collision.

Adversarial interactions lead to safer self-driving policies in simulation.

Abstract

Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self driving. To this end, we introduce a "Physically Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact aggressively in the CARLA simulation environment. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The coupled networks alternately seek-to-collide and to avoid collisions such that the "defensive" avoidance algorithm increases the mean-time-to-failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary.