# Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion

arXiv: https://arxiv.org/abs/2610.13872 · HTML: https://mlsanity.com/paper/2610.13872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Builds on (most cited first, 14 of 14)

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- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
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- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Safe Robot Navigation via Multi-Modal Anomaly Detection](https://mlsanity.com/paper/2001.07934/) (2020)
- [WayFAST: Navigation with Predictive Traversability in the Field](https://mlsanity.com/paper/2203.12071/) (2022)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [Find Your Own Way: Weakly-Supervised Segmentation of Path Proposals for Urban Autonomy](https://mlsanity.com/paper/1610.01238/) (2016)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain](https://mlsanity.com/paper/2208.01329/) (2022)
- [Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned](https://mlsanity.com/paper/2111.13981/) (2021)
- [Improving Visual Feature Extraction in Glacial Environments](https://mlsanity.com/paper/1908.10425/) (2019)

## Abstract (arXiv)

Accurate detection of drivable area is essential for autonomous driving and advanced driver assistance systems. While state-of-the-art methods focus on general traversability estimation, they prove inadequate for snow-covered roads where following existing wheel tracks offers crucial advantages: improved traction, predictable vehicle dynamics, and verified path stability. To address this, we propose a low-cost, self-supervised approach detecting safety-critical wheel tracks using synchronized RGB-I (RGB + Long-Wave Infrared) imagery. Our method automatically generates training labels by projecting future wheel trajectories from recorded GNSS data. A neural network is designed to identify wheel tracks with the 4-channel RGB-I images from challenging, white-out road surfaces. Experimental results demonstrate the effectiveness of our approach in visually monotonous and featureless adverse environment, offering a robust and cost-effective alternative for winter autonomy. The newly introduced ThermalTrack dataset is available at: https://digitalcommons.mtu.edu/thermaltrack/.