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

• URL: http://arxiv.org/abs/2003.10662v1
• Date: Tue, 24 Mar 2020 05:04:13 GMT
• Title: Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)
• Authors: Piyush Gupta, Demetris Coleman, Joshua E. Siegel
• Abstract summary: We introduce a "Physically Adrial Intelligent Network" (PAIN) wherein self-driving vehicles interact aggressively. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures.
• Score: 3.136861161060885
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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