Journal ArticleDOI
FullStop: A Camera-Assisted System for Characterizing Unsafe Bus Stopping
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TLDR
FullStop is a smartphone-based system that detects safety risks emanating from stopping behavior like the ones listed above, and it is shown that the GPS and inertial sensors are unable to perform the fine-grained detection needed.Abstract:
Road safety is a critical issue worldwide. We believe that mobile devices can play a positive role in this context by detecting dangerous conditions and providing feedback. This paper focuses on a specific problem in developing countries: the stopping behaviour of buses in the vicinity of bus stops. For instance, buses could arrive at a bus stop but continue rolling forward instead of coming to a complete halt, or could stop some distance away from the bus stop, possibly even in the middle of a busy road. Such behaviors put at risk the passengers boarding or alighting the bus, and also the people waiting at a bus stop. We present FullStop, a smartphone-based system that detects safety risks emanating from stopping behavior like the ones listed above. We show that the GPS and inertial sensors are unable to perform the fine-grained detection needed. Therefore, our approach in FullStop is based on the view obtained from looking out to the front of the vehicle using the camera of a smartphone that is mounted on the front windshield. Using optical flow vectors, with several refinements, FullStop running on a smartphone is able to effectively detect various unsafe bus stopping behaviours.read more
Citations
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Data-driven approaches for road safety: A comprehensive systematic literature review
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Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application
Subhadeep Patra,David Van Hamme,Peter Veelaert,Carlos T. Calafate,Juan-Carlos Cano,Pietro Manzoni,Willian Zamora +6 more
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Exploiting Multi-modal Contextual Sensing for City-bus’s Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction
TL;DR: In this paper , the authors developed a system for extracting and characterizing the stay locations from multi-modal sensing using commuters' smartphones, which can identify different stay locations like regular bus stops, random ad-hoc stops, stops due to traffic congestion stops at traffic signals, and stops at sharp turns.
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Emergency Vehicle-Based Vehicle Detection Model
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TL;DR: In this paper, a three-stage algorithm is presented to calibrate roadside traffic management cameras and track vehicles to create a traffic speed sensor, where the camera position relative to the roadway is estimated using the motion and edges of the vehicles.
Journal ArticleDOI
Driver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring
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Proceedings ArticleDOI
CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones
Chuang-Wen You,Nicholas D. Lane,Fanglin Chen,Rui Wang,Zhenyu Chen,Thomas J. Bao,Martha Montes-de-Oca,Yuting Cheng,Mu Lin,Lorenzo Torresani,Andrew T. Campbell +10 more
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