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This paper comprises obtaining friction coefficient (μ) measure by extracting surface and texture information using sensors during brake interventions. A primary estimate of friction coefficient has been obtained using wheel and vehicle signals. The estimates have been compared and combined to obtain a more accurate measure of friction coefficient. Finally, a suitable interpolation technique is used to obtain a μ-grid around the vehicle. The grid is graphically realized with the aid of visualization techniques using vehicle traces. This type of surface characterization usually enables brake distance optimization and effective countermeasures pertaining to a standard ESP system.

This paper will discuss information extraction from sound signals obtained using acoustic sensors which are strategically placed on the automotive body. The sound signal obtained contains useful information such as vehicle and road surface characteristics. The aforementioned information was spread out at different frequencies in the spectral distribution of the signal. Well defined filters for the corresponding frequency bands were used to isolate these characteristics and patterns pertaining to the identified useful information from the signal. The obtained information can be used as inputs for systems such as vehicle safety systems, power train systems and NVH (Noise, Vibration and Harshness) systems.