Learning Deep Neural Network Control Policies for Agile Off-Road Autonomous Driving
نویسندگان
چکیده
We present an end-to-end learning system for agile, off-road autonomous driving using only low-cost on-board sensors. By imitating an optimal controller, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands, the latter of which is essential to successfully drive on varied terrain at high speed. Compared with recent approaches to similar tasks, our method requires neither state estimation nor online planning to navigate the vehicle. Real-world experimental results demonstrate successful autonomous driving, matching the state-of-the-art performance.
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