Study on the Effect of Electrical Conductivity on Road Surface Wetness Classification Using Capacitive Measurement Data

Abstract

As the friction between a vehicle’s tires and the road surface correlates with the road-covering water film height, knowledge about the present wetness level is of relevance for drivers and autonomous systems. A promising approach for wetness quantification is based on a capacitive transducer array (TA) that is integrated at a front wheel arch liner and capable of detecting spray water ejected by the tires. While this approach has already shown that wetness classification using capacitive measurement data is feasible, potentially affecting factors such as the water’s electrical conductivity were assumed to be constant in the past. This article presents a study on the effect of the spray water’s conductivity on wetness classification for that approach and also demonstrates the feasibility of classifying conductivity using the same capacitive measurement data. For these purposes, we propose a test bench capable of simulating reproducible spray water scenarios. We show the effect of varying conductivity on the measured capacitance curves complicating the distinction of wetness classes. In partial investigations, we demonstrate the extent of the conductivity’s effect on classifier performance. While wetness classification with conductivities differing from training results in poor classifier performance, the mean accuracy (ACC) can be increased to approximately 0.98 considering all occurring conductivities. Furthermore, we propose a two-stage classification approach that initially determines the conductivity and subsequently considers it as an additional feature in wetness classification. The approach increases classifier performance to more than 0.99 indicating that knowledge of the spray’s present conductivity can optimize road surface wetness classification.