Embedded Acoustic Intelligence for Automotive Systems
Abstract
Transforming sound insights into actionable streams of data, this abstract leverages findings from degree thesis research to enhance automotive system intelligence, enabling us to address road type [1].By extracting and interpreting acoustic signatures from microphones installed within the wheelbase of a car, we focus on classifying road type.Utilizing deep neural networks and feature extraction powered by pre-trained models from the Open AI ecosystem (via Hugging Face [2]), our approach enables Autonomous Driving and Advanced Driver- Assistance Systems (AD/ADAS) to anticipate road surfaces, support adaptive learning for active road noise cancellation, and generate valuable insights for urban planning. The results of this study were specifically captured to support a compelling business case for next-generation automotive systems. This forward-looking approach not only promises to redefine passenger comfort and improve vehicle safety, but also paves the way for intelligent, data-driven urban road management, making the future of mobility both achievable and sustainable.
Cite
@article{arxiv.2506.11071,
title = {Embedded Acoustic Intelligence for Automotive Systems},
author = {Renjith Rajagopal and Peter Winzell and Sladjana Strbac and Konstantin Lindström and Petter Hörling and Faisal Kohestani and Niloofar Mehrzad},
journal= {arXiv preprint arXiv:2506.11071},
year = {2025}
}