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1. PAWS: A Wearable Acoustic System for Pedestrian Safety

   de Godoy, Daniel; Islam, Bashima; Xia, Stephen; Islam, Md Tamzeed; Chandrasekaran, Rishikanth; Chen, Yen-Chun; Nirjon, Shahriar; Kinget, Peter; Jiang, Xiaofan (April 2018, International Conference on Internet-of-Things Design and Implementation)

   With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this paper, we build a wearable system that uses multi-channel audio sensors embedded in a headset to help detect and locate cars from their honks, engine and tire noises, and warn pedestrians of imminent dangers of approaching cars. We demonstrate that using a segmented architecture and implementation consisting of headset-mounted audio sensors, a front-end hardware that performs signal processing and feature extraction, and machine learning based classification on a smartphone, we are able to provide early danger detection in real-time, from up to 60m distance, near 100% precision on the vehicle detection and alert the user with low latency. 

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2. A Smartphone-Based System for Improving Pedestrian Safety

   https://doi.org/10.1109/VNC.2018.8628320  

   Xia, Stephen; de Godoy, Daniel; Islam, Bashima; Islam, Md Tamzeed; Nirjon, Shahriar; Kinget, Peter R.; Jiang, Xiaofan (December 2018, 2018 IEEE Vehicular Networking Conference (VNC))

   With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this article, we present PAWS, a smartphone platform that utilizes an embedded wearable headset system mounted with an array of MEMS microphones to help detect, localize, and warn pedestrians of the imminent dangers of approaching cars. 

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3. CSafe: An Intelligent Audio Wearable Platform for Improving Construction Worker Safety in Urban Environments

   https://doi.org/10.1145/3412382.3458267  

   Xia, Stephen; Nie, Jingping; Jiang, Xiaofan (May 2021, Proceedings of the 20th International Conference on Information Processing in Sensor Networks)

   null (Ed.)

   Vehicle accidents are one of the greatest cause of death and injury in urban areas for pedestrians, workers, and police alike. In this work, we present CSafe, a low power audio-wearable platform that detects, localizes, and provides alerts about oncoming vehicles to improve construction worker safety. Construction worker safety is a much more challenging problem than general urban or pedestrian safety in that the sound of construction tools can be up to orders of magnitude greater than that of vehicles, making vehicle detection and localization exceptionally difficult. To overcome these challenges, we develop a novel sound source separation algorithm, called Probabilistic Template Matching (PTM), as well as a novel noise filtering architecture to remove loud construction noises from our observed signals. We show that our architecture can improve vehicle detection by up to 12% over other state-of-art source separation algorithms. We integrate PTM and our noise filtering architecture into CSafe and show through a series of real-world experiments that CSafe can achieve up to an 82% vehicle detection rate and a 6.90° mean localization error in acoustically noisy construction site scenarios, which is 16% higher and almost 30° lower than the state-of-art audio wearable safety works. 

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4. Toward audio-based sensing for pedestrian detection

   https://doi.org/10.1121/10.0027509  

   Ding, Yiwei; Han, Chaeyeon; Seshadri, Pavan; Koo, Bon Woo; Posner, Noah; Guhathakurta, Subhro; Lerch, Alexander (March 2024, The Journal of the Acoustical Society of America)

   The detection and counting of pedestrians plays a central role for the design of smart cities. Although the use of cameras for this task has been shown to have high accuracy, they come at a high cost and are susceptible to challenges such as poor lighting, fog, and obstructed views. Our study investigates audio-based pedestrian detection, combining potentially low cost sensors with advanced machine learning based audio analysis algorithms. With an audio sensor installed along the walkway, machine learning algorithms can tell from the audio whether there is a pedestrian or not, or how far the pedestrian is from the sensor. Results show the general feasibility of audio-based pedestrian detection but fall short of reaching the accuracy levels of video-based detection. 

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5. ADOPT: A system for Alerting Drivers to Occluded Pedestrian Traffic

   https://doi.org/10.1016/j.vehcom.2023.100601  

   Alali, Abrar; Olariu, Stephan; Jain, Shubham (June 2023, Vehicular Communications)

   Recent statistics reveal an alarming increase in accidents involving pedestrians (especially children) crossing the street. A common philosophy of existing pedestrian detection approaches is that this task should be undertaken by the moving cars themselves. In sharp departure from this philosophy, we propose to enlist the help of cars parked along the sidewalk to detect and protect crossing pedestrians. In support of this goal, we propose ADOPT: a system for Alerting Drivers to Occluded Pedestrian Traffic. ADOPT lays the theoretical foundations of a system that uses parked cars to: (1) detect the presence of a group of crossing pedestrians – a crossing cohort; (2) predict the time the last member of the cohort takes to clear the street; (3) send alert messages to those approaching cars that may reach the crossing area while pedestrians are still in the street; and, (4) show how approaching cars can adjust their speed, given several simultaneous crossing locations. Importantly, in ADOPT all communications occur over very short distances and at very low power. Our extensive simulations using SUMO-generated pedestrian and car traffic have shown the effectiveness of ADOPT in detecting and protecting crossing pedestrians. 

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