外观
论文arXiv 2610.1387219 天前
追踪不可见痕迹：基于热成像-RGB融合的自监督车轮轨迹检测
原文链接：Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion（arXiv 2610.13872 · 发布日期：2026-10-09）
Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion
针对雪覆盖道路可行驶区域检测，提出RGB-I自监督车轮轨迹检测方法，并在恶劣白化路面实验验证有效性。
核心要点
- 雪覆盖道路上，跟随已有车轮轨迹有助于提升牵引力、车辆动态可预测性和路径稳定性。
- 方法使用同步RGB-I图像，并由记录的GNSS数据投影未来车轮轨迹，自动生成训练标签。
- 设计神经网络从四通道RGB-I图像中识别白化、单调且缺少特征路面上的车轮轨迹。
- 实验显示该方法在视觉单调的恶劣环境中有效，ThermalTrack数据集已公开。
验证范围与局限：验证范围为雪覆盖、白化等视觉单调恶劣道路环境下的RGB-I车轮轨迹检测。
英文摘要 (Abstract)
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/.
作者：Yiming Yang, Jeremy P. Bos
来源
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