Road Grip Uncertainty Estimation Through Surface State Segmentation
This work addresses safety challenges in autonomous driving by improving grip uncertainty estimation, though it appears incremental as it builds on existing uncertainty prediction methods.
The paper tackles the problem of slippery road conditions for autonomous driving by benchmarking uncertainty prediction methods and proposing a novel approach that uses road surface state segmentation to predict grip uncertainty, resulting in enhanced robustness of grip uncertainty prediction.
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.