Embedded Acoustic Intelligence for Automotive Systems

TL;DR

This paper presents an acoustic-based system using deep learning to classify road types from vehicle-mounted microphones, enhancing automotive intelligence for safety, comfort, and urban planning.

Contribution

It introduces a novel approach leveraging pre-trained neural networks for road type classification from acoustic signatures in automotive systems.

Findings

Effective road type classification using deep neural networks.

Supports adaptive noise cancellation and urban planning insights.

Enhances autonomous driving and AD/ADAS capabilities.

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.