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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. Improving Pedestrian Safety in Cities using Intelligent Wearable Systems

   https://doi.org/10.1109/JIOT.2019.2903519  

   Xia, Stephen; de Godoy, Daniel; Islam, Bashima; Islam, Md Tamzeed; Nirjon, Shahriar; Kinget, Peter R.; Jiang, Xiaofan (March 2019, IEEE Internet of Things Journal)

   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 consisting of headset-mounted audio sensors, a front-end hardware platform 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 away, and alert the user with low latency and high accuracy. To further reduce power consumption of the battery-powered wearable headset, we implement a custom-designed integrated circuit that is able to compute delays between multiple channels of audio with nW power consumption. A regression-based method for sound source localization, AvPR, is proposed and used in combination with the IC to improve the granularity and robustness of localization. 

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3. 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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4. Feelings of Culpability: Just Following Orders Versus Making the Decision Oneself

   https://doi.org/10.1177/09567976211002821  

   Malter, Maayan_S; Kim, Sonia_S; Metcalfe, Janet (April 2021, Psychological Science)

   In five experiments ( N = 1,490), participants were asked to imagine themselves as programmers of self-driving cars who had to decide how to program the car to respond in a potential accident: spare the driver or spare pedestrians. Alternatively, participants imagined that they were a mayor grappling with difficult moral dilemmas concerning COVID-19. Either they, themselves, had to decide how to program the car or which COVID-19 policy to implement (high-agency condition) or they were told by their superior how to act (low-agency condition). After learning that a tragic outcome occurred because of their action, participants reported their felt culpability. Although we expected people to feel less culpable about the outcome if they acted in accordance with their superior’s injunction than if they made the decision themselves, participants actually felt more culpable when they followed their superior’s order. Some possible reasons for this counterintuitive finding are discussed. 

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5. Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection

   You, Yurong; Diaz-Ruiz, Carlos A.; Wang, Yan; Chao, Wei-Lun; Hariharan, Bharath; Campbell, Mark; Weinberger, Kilian Q. (April 2022, Proceedings of the IEEE International Conference on Robotics and Automation)

   Self-driving cars must detect other traffic partici- pants like vehicles and pedestrians in 3D in order to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit domain idiosyncrasies, making them fail in new environments—a serious problem for the robustness of self-driving cars. In this paper, we propose a novel learning approach that reduces this gap by fine-tuning the detector on high-quality pseudo-labels in the target domain – pseudo- labels that are automatically generated after driving based on replays of previously recorded driving sequences. In these replays, object tracks are smoothed forward and backward in time, and detections are interpolated and extrapolated— crucially, leveraging future information to catch hard cases such as missed detections due to occlusions or far ranges. We show, across five autonomous driving datasets, that fine-tuning the object detector on these pseudo-labels substantially reduces the domain gap to new driving environments, yielding strong improvements detection reliability and accuracy. 

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