2017

Agile Autonomous Driving using End-to-End Deep Imitation Learning

Pan, Yunpeng, Cheng, Ching-An, Saigol, Kamil et al.

Understand

We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors.

By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands.
Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle.
Our approach relies on, and experimentally validates, recent imitation learning theory.

Built on

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