Mapping Tire-Road Friction Coefficients Along Driven Traject
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Method Maps Tire-Road Friction Along Driving Trajectories. FrictionMap estimates friction coefficients from recorded data using Kalman filters and Gaussian processes. It outperforms online methods in simulation and is open-sourced post-acceptance.
Generated from attributed reports · Updated 35 minutes ago
Event evidence and corrections
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Environment: simulation
“shold as prior knowledge. FrictionMap runs an extended Kalman filter combined with a Rauch-Tung-Striebel smoother to estimate vehicle dynamics. Data points where the friction limit is observable are identified by filtering points based on tire slip. A Gaussian process is fitted to these points to acquire a continuous estimate of the friction coefficient along the trajectory. FrictionMap is validated via a simulation”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsRAGE: Radar-Aided Grip Estimator for Autonomous Race Cars
RAGE estimates vehicle grip using standard sensors, enabling real-time performance in autonomous race cars. Validated through simulations and real-world tests on EAV-24.
- arXiv Robotics — research abstractsMethod Maps Tire-Road Friction Along Driving Trajectories
FrictionMap estimates friction coefficients from recorded data using Kalman filters and Gaussian processes. It outperforms online methods in simulation and is open-sourced post-acceptance.
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