Detecting Road Tunnel-Like Environments Using Acoustic Classification for Sensor Fusion with Radar Systems

Radar systems equipped with Misalignment Monitoring and Adjustment (MM &A) face challenges in accurately functioning within complex environments, particularly tunnels. Standard radar system design assumes constant background activity of the MM &A throughout a host vehicle’s ignition cycle, monitoring for misaligned radar sensors and mitigating issues associated with faulty radar measurements. However, the presence of tunnels and other unfavorable driving conditions can influence MM &A, thereby affecting its performance. To address this issue, it is crucial to develop a reliable method for detecting tunnel-like environments and appropriately adjusting the MM &A system. This research paper focuses on the novel acoustic sensing system called SONETE (Sonic Sensing for Tunnel Environment) for classification of acoustic signatures recorded by pressure zone microphone to accurately identify tunnel environments. The study aims to explore acoustic features and classification algorithms to distinguish between road and tunnel environment and using a sensor fusion with radar systems, suspend the MM &A system accordingly. By tackling this problem, the research contributes to the advancement of intelligent transportation systems by enhancing radar technology’s robustness in complex environments and ensuring effective MM &A adjustments in tunnels. Overall, this paper demonstrates the potential of using acoustic signatures as a complementary sensor for tunnel detection in vehicles where traditional sensors have limitations.