通过路面状态分割提升道路抓地力不确定性估计的可靠性
Road Grip Uncertainty Estimation Through Surface State Segmentation
- 利用路面状态分割生成像素级抓地力概率分布
- 实验显示新方法显著提升不确定性预测鲁棒性
- 适合自动驾驶系统安全控制场景使用
湿滑路面条件对自动驾驶构成重大挑战。除了预测道路抓地力外，可靠估计其不确定性对于确保车辆安全控制至关重要。本文评估了几种不确定性预测方法在抓地力不确定性估计中的有效性，并提出一种新方法：利用道路表面状态分割来预测抓地力不确定性。该方法基于推断的道路表面状况，估计像素级抓地力概率分布。实验结果表明，所提方法显著提升了抓地力不确定性预测的鲁棒性。
原文摘要 · Abstract (English)
Slippery road conditions pose significant challenges for autonomous driving. Beyond predicting road grip, it is crucial to estimate its uncertainty reliably to ensure safe vehicle control. In this work, we benchmark several uncertainty prediction methods to assess their effectiveness for grip uncertainty estimation. Additionally, we propose a novel approach that leverages road surface state segmentation to predict grip uncertainty. Our method estimates a pixel-wise grip probability distribution based on inferred road surface conditions. Experimental results indicate that the proposed approach enhances the robustness of grip uncertainty prediction.
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