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

arXiv: https://arxiv.org/abs/2207.01275 · HTML: https://mlsanity.com/paper/2207.01275/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [Critic Guided Segmentation of Rewarding Objects in First-Person Views](https://mlsanity.com/paper/2107.09540/) (2021)
- [Solving Physics Puzzles by Reasoning about Paths](https://mlsanity.com/paper/2011.07357/) (2020)
- [Modularization of End-to-End Learning: Case Study in Arcade Games](https://mlsanity.com/paper/1901.09895/) (2019)

## Built on by (most cited first, 4 of 4)

- [Behavioral Cloning via Search in Video PreTraining Latent Space](https://mlsanity.com/paper/2212.13326/) (2022)
- [Planning with RL and episodic-memory behavioral priors](https://mlsanity.com/paper/2207.01845/) (2022)
- [Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks](https://mlsanity.com/paper/2312.05925/) (2023)
- [Behavioral Cloning via Search in Embedded Demonstration Dataset](https://mlsanity.com/paper/2306.09082/) (2023)

## Abstract (arXiv)

Learn-to-Race Autonomous Racing Virtual Challenge hosted on www<dot>aicrowd<dot>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.