The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/07c5956a-fd5f-411f-9b4e-ffe7eb7dec5f

Schulz, Yannick; Kini Mattar, Avinash; Hehn, Thomas; Kooij, Julian (2024): OVAD - Occluded Vehicle Acoustic Detection dataset. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/07c5956a-fd5f-411f-9b4e-ffe7eb7dec5f.v1

Dataset

• Artificial Intelligence and Image Processing
• Other Built Environment and Design
• Other Information and Computing Sciences
• Ground Transport
• Transport
• Built Environment and Design
• Information and Computing Sciences

acoustics, audio, beam forming, machine learning, microphone array, multi-channel sound, robotics, self driving, traffic sound, vehicle sound

CC BY-NC-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Yannick Schulz , Avinash Kini Mattar , Thomas Hehn , Julian Kooij

Acoustic multi-microphone dataset (plus video) from the paper "Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners" (IEEE Robotics and Automation Letters 2021, see DOI: 10.1109/LRA.2021.3062254 and arxiv). For a short and intuitive introduction of our main ideas and online prediction results, we recommend to watch our supplementary video on Youtube: Hearing What You Cannot See.

We present a data-driven acoustic detection method that can detect an approaching vehicle before it enters line-of-sight, e.g. while hidden behind a blind corner, in real-world outdoor conditions. The code can be found at github.com/tudelft-iv/occluded_vehicle_acoustic_detection .

As part of this publication, we provide a novel audio-visual dataset (OVAD - occluded vehicle acoustic detection) collected in outdoor urban environments with a 56-microphone array mounted on our research vehicle.

• 2024-10-08 first online, published, posted

4TU.ResearchData

wav; mp4; csv; json

Hearing What You Cannot See: Acoustic Vehicle Detection Around Corners

TU Delft, Faculty of Mechanical Engineering, Department of Cognitive Robotics, Intelligent Vehicles Group