Road Type Classification Using Time-Frequency Representations of Tire Sensor Signals

Abstract

Smart tire technologies offer a novel sensing methodology for vehicle environment perception by providing direct measurements of tire dynamics parameters. This information can be utilized in advanced driver assistance systems as well as autonomous vehicle control to enhance vehicle performance and safety. Considering these criteria, we develop algorithms for categorizing road types based on tire sensor signals.Road differentiation is a complex task due to the non-linear and non-stationary nature of the measured tire signals. To address this challenge, we fuse time-frequency distributions with machine learning approaches. Maintaining the robustness of the predictions, we integrate our own measurement system into a Nissan Leaf test vehicle and collect data involving diverse environmental factors and operational conditions, mimicking real-world scenarios. We showed that by experiments our predictions strongly correlate with road quality, which can be utilized in automatic vehicle control, such as intelligent speed adaptation.

Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals

“…In various scientific domains, such as gravitational wave detection [1], radar systems [2,3], tire sensor signal processing [4] and biomedical signal processing [5,6], signals frequently exhibit nonlinear frequency modulation (FM), characterized by time-dependent frequencies known as instantaneous frequencies (IFs). Time-frequency distributions (TFDs) are essential tools for representing signal energy in the joint time-frequency (TF) domain [7].…”