Mapping tire-road friction accurately along driven routes
Mapping Tire-Road Friction Coefficients Along Driven Trajectories
Robotics
Summary
Knowing how slippery the road is can help vehicles stay safe by understanding how much grip their tires have. The authors developed a method called FrictionMap that looks at data collected during driving to create a detailed map of road slipperiness after the drive is done. This method uses common car sensors and some math tools to estimate where the tires might lose grip. It works better than existing methods tested in a simulation. The code is shared openly so others can build on this work.
What this means in practice
- •For automotive engineers: Create detailed friction maps from driven data to improve vehicle control systems' response on low-friction road segments.
- •For fleet operation teams: Use friction maps shared among connected vehicles to anticipate and avoid risky areas with reduced tire grip during route planning.
Authors
Risto Ojala
Abstract
Information of the tire-road friction coefficient is critical for intelligent vehicle systems, as friction establishes the limits at which the vehicle can be manoeuvred without loss of control. While there is extensive literature on online friction estimation, no previous studies have explored post-processing and mapping friction coefficients along a driven trajectory based on recorded sensor data. Mapped friction information could be shared by connected vehicles, enabling proactive measures in low-friction areas. This paper presents an offline post-processing method, FrictionMap, for mapping friction coefficients from recorded driving data. The method operates on commonly available on-board vehicle sensor information, and requires only a basic vehicle parameter set and a slip threshold 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 study, and shown to outperform commonly used online friction estimation techniques. The code implementation and the simulation benchmark are released as open-source after paper acceptance to enable further research.