Abstract
Slippery road weather conditions are prevalent in many regions and cause a regular risk for traffic. Still, there has been less research on how autonomous vehicles could detect slippery driving conditions on the road to drive safely. In this work, we propose a method to predict a dense grip map from the area in front of the car, based on postprocessed multimodal sensor data. We trained a convolutional neural network to predict pixelwise grip values from fused RGB camera, thermal camera, and LiDAR reflectance images, based on weakly supervised ground truth from an optical road weather sensor. The experiments show that it is possible to predict dense grip values with good accuracy from the used data modalities as the produced grip map follows both ground truth measurements and local weather conditions, such as snowy areas on the road. The model using only the RGB camera or LiDAR reflectance modality provided good baseline results for grip prediction accuracy while using models fusing the RGB camera, thermal camera, and LiDAR modalities improved the grip predictions significantly.
Abstract (translated)
许多地区都普遍存在滑坡路况，这对交通安全造成了 regular 的风险。然而，关于自动驾驶汽车如何从车辆前方的区域预测滑坡路况以安全驾驶的研究还相对较少。在这项工作中，我们提出了一种基于 postprocessed multimodal sensor data 预测汽车前方区域稀疏抓地图的方法。我们训练了一个卷积神经网络，根据弱监督的地面真实值从融合 RGB 相机、热成像和激光雷达反照像预测像素级别的抓地值。实验结果表明，使用所提供的数据模态，可以预测出良好的抓地值，且生成的抓地图既符合地面真实测量值，又考虑了道路当地的天气状况，例如道路上的积雪区域。使用仅 RGB 相机或 LiDAR 反照像模型的模型，提供了良好的基线抓地预测精度，而将 RGB 相机、热成像和激光雷达模型的融合模型显著提高了抓地预测精度。
URL
https://arxiv.org/abs/2404.17324
