Road Grip Uncertainty Estimation Through Surface State Segmentation

2025-04-11

AI Summary

Addressing the challenge of modeling tire-road grip uncertainty under slippery road conditions in autonomous driving, this paper proposes a semantic-segmentation-guided method for pixel-wise probabilistic grip distribution prediction. Our approach explicitly incorporates road-state segmentation outputs into the uncertainty estimation pipeline, establishing an end-to-end framework that integrates a semantic segmentation backbone, a probabilistic output head, and multi-source uncertainty benchmarks. The method enables calibrated, pixel-level grip confidence estimation. Evaluated on real-world road datasets, it achieves significant improvements in uncertainty calibration—reducing the Expected Calibration Error (ECE) by 37%—and out-of-distribution (OOD) detection accuracy for slippery conditions—increasing accuracy by 22%. These advances provide a reliable, interpretable foundation for grip uncertainty perception, directly supporting safety-critical vehicle control.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Estimating road grip uncertainty for autonomous driving safety
• Benchmarking methods for effective grip uncertainty prediction
• Proposing surface segmentation to improve grip uncertainty robustness

Innovation

• Benchmarking uncertainty prediction methods for grip estimation
• Using road surface segmentation for grip uncertainty prediction
• Estimating pixel-wise grip probability from surface conditions