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arXiv · 2207.01275

Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space

Shivansh Beohar·Fabian Heinrich·Rahul Kala·Helge Ritter·Andrew Melnik
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Abstract

Learn-to-Race Autonomous Racing Virtual Challenge hosted on www aicrowd com platform consisted of two tracks: Single and Multi Camera. Our UniTeam team was among the final winners in the Single Camera track. The agent is required to pass the previously unknown F1-style track in the minimum time with the least amount of off-road driving violations. In our approach, we used the U-Net architecture for road segmentation, variational autocoder for encoding a road binary mask, and a nearest-neighbor search strategy that selects the best action for a given state. Our agent achieved an average speed of 105 km/h on stage 1 (known track) and 73 km/h on stage 2 (unknown track) without any off-road driving violations. Here we present our solution and results.

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Shivansh Beohar, Fabian Heinrich, Rahul Kala, Helge Ritter, Andrew Melnik. 2022-07-04. Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space. https://arxiv.org/abs/2207.01275

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