Accelerated Testing and Evaluation of Autonomous Vehicles via Imitation Learning

2018 IEEE International Conference on Robotics and Automation (ICRA)


Abstract

In this paper, we investigate the use of surrogate agents to accelerate test scenario generation for autonomous vehicles. Our goal is to train the surrogate to replicate the true performance modes of the system. We create these surrogates by utilizing imitation learning with deep neural networks. By using imitator surrogates in place of the true agent, we are capable of predicting mission performance more quickly, gaining greater throughput for simulation-based testing. We demonstrate that using on-line imitation learning with Dataset Aggregation (DAgger) can not only correctly encode a policy that executes a complex mission, but can also encode multiple different behavioral modes. To improve performance for the target vehicle and mission, we manipulate the training set during each iteration to remove samples which do not contribute to the final policy. We call this approach Quantile-DAgger (Q-DAgger) and demonstrate its ability to replicate the behaviors of an autonomous vehicle in a collision avoidance scenario.

Citation

@inproceedingsMullins_2018 doi: 10.1109/icra.2018.8460965 url: https://doi.org/10.1109/icra.2018.8460965 year: 2018 month: may publisher: IEEE author: Mullins Galen E. and Dress Austin G. and Stankiewicz Paul G. and Appler Jordan D. and Gupta Satyandra K. title: Accelerated Testing and Evaluation of Autonomous Vehicles via Imitation Learning booktitle: 2018 IEEE International Conference on Robotics and Automation (ICRA)

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