# Emergency Vehicles Audio Detection and Localization in Autonomous Driving

arXiv: https://arxiv.org/abs/2109.14797 · HTML: https://mlsanity.com/paper/2109.14797/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [Are Pre-trained Convolutions Better than Pre-trained Transformers?](https://mlsanity.com/paper/2105.03322/) (2021)
- [Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates](https://mlsanity.com/paper/1807.11094/) (2018)
- [PILOT: Introducing Transformers for Probabilistic Sound Event Localization](https://mlsanity.com/paper/2106.03903/) (2021)
- [Lightweight and Optimized Sound Source Localization and Tracking Methods for Open and Closed Microphone Array Configurations](https://mlsanity.com/paper/1812.00115/) (2018)

## Built on by (most cited first, 5 of 5)

- [OmniBench: Towards The Future of Universal Omni-Language Models](https://mlsanity.com/paper/2409.15272/) (2024)
- [XAI-based Comparison of Input Representations for Audio Event Classification](https://mlsanity.com/paper/2304.14019/) (2023)
- [Multifaceted Evaluation of Audio-Visual Capability for MLLMs: Effectiveness, Efficiency, Generalizability and Robustness](https://mlsanity.com/paper/2504.16936/) (2025)
- [Improving the Environmental Perception of Autonomous Vehicles using Deep Learning-based Audio Classification](https://mlsanity.com/paper/2209.04075/) (2022)
- [Looking and Listening Inside and Outside: Multimodal Artificial Intelligence Systems for Driver Safety Assessment and Intelligent Vehicle Decision-Making](https://mlsanity.com/paper/2602.07668/) (2026)

## Abstract (arXiv)

Emergency vehicles in service have right-of-way over all other vehicles. Hence, all other vehicles are supposed to take proper actions to yield emergency vehicles with active sirens. As this task requires the cooperation between ears and eyes for human drivers, it also needs audio detection as a supplement to vision-based algorithms for fully autonomous driving vehicles. In urban driving scenarios, we need to know both the existence of emergency vehicles and their relative positions to us to decide the proper actions. We present a novel system from collecting the real-world siren data to the deployment of models using only two cost-efficient microphones. We are able to achieve promising performance for each task separately, especially within the crucial 10m to 50m distance range to react (the size of our ego vehicle is around 5m in length and 2m in width). The recall rate to determine the existence of sirens is 99.16% , the median and mean angle absolute error is 9.64deg and 19.18deg respectively, and the median and mean distance absolute error of 9.30m and 10.58m respectively within that range. We also benchmark various machine learning approaches that can determine the siren existence and sound source localization which includes direction and distance simultaneously within 50ms of latency.