Machine learning algorithms for wet road surface detection using acoustic measurements

Precipitation can adversely influence road safety. Slippery road conditions have traditionally been detected using reactive methods requiring considerable excitation of the tire forces. Alternatives rely on non-contact methods such as vision, sound or ultrasonic sensors. This study proposes a cost-effective wet road conditions detection method based on acoustic measurements for urban and highway driving. It compared the performance of a range of machine learning algorithms to classify the road condition based on the audio features calculated using octave-band frequency analysis. The approach was evaluated experimentally using data collected from a vehicle instrumented with a microphone, GPS and CAN bus data logger. Support Vector Machines using Quadratic and Cubic kernels, as well as Logistic Regression performed better compared to other machine learning-based methods.