Binary Road Surface Classification Using Machine Learning on Production Vehicle Signals During Cruising
arXiv:2606.02762v1 Announce Type: new Abstract: Knowledge of real-time road slipperiness, or even better, a refined estimate of peak grip potential, is a critical input for vehicle warning and intervention control systems. Typically, friction is estimated through dynamics-based recursive estimators by calculating the slip slope; however, its efficacy is heavily constrained by the vehicle dynamic scenario. When the vehicle is cruising and there is little to no slip, these methods become ineffective due to the inability of present-day production-grade sensors, such as wheel speed sensors, and me
Advances in machine learning and accessible automotive sensor data are enabling new applications for vehicle safety and autonomy.
This development enhances real-time road condition awareness, crucial for autonomous driving systems and advanced vehicle safety features.
Traditional friction estimation methods are supplemented by ML-based approaches, improving reliability and robustness, especially in low-slip conditions.
- · Autonomous vehicle developers
- · Automotive safety systems manufacturers
- · Insurance companies
- · Sensor manufacturers
Improved vehicle safety and performance through enhanced road condition awareness.
Faster development and deployment of Level 3+ autonomous driving systems due to more reliable environmental perception.
Reduced accident rates and potentially lower insurance premiums over time as these technologies become widespread.
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