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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.

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Citations
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Journal ArticleDOI

Data-driven approaches for road safety: A comprehensive systematic literature review

TL;DR: In this paper , a detailed review of 70 articles, which are shortlisted from 2871 articles found by searching relevant keywords from the scopus IEEE digital library and google scholar databases, is presented.
Journal ArticleDOI

Detecting Vehicles’ Relative Position on Two-Lane Highways Through a Smartphone-Based Video Overtaking Aid Application

TL;DR: A smartphone-based real-time video overtaking architecture for vehicular networks that aims to prevent head-on collisions that might occur due to attempts to overtake when the view of the driver is obstructed by the presence of a larger vehicle ahead.
Journal ArticleDOI

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.
Journal ArticleDOI

Ethical AI for Automated Bus Lane Enforcement

TL;DR: In this article, a use case is presented which examines the ethical data required to automatically enforce bus lanes using camera surveillance and proposes ways of minimising the risks of privacy infringement and erosion in that scenario.
Book ChapterDOI

Emergency Vehicle-Based Vehicle Detection Model

TL;DR: In this paper , the authors have developed a very unique solution and try to find unsolved issues in more technical way, which is about tracing follower vehicles of emergency vehicles using RFID (radio frequency identification).
References
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Journal ArticleDOI

Activity recognition with smartphone sensors

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Journal ArticleDOI

The use of optical flow for road navigation

TL;DR: The results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.
Journal ArticleDOI

Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation

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

TL;DR: This paper analyzes how smartphone sensors can be used to identify driving maneuvers and proposes SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle.
Proceedings ArticleDOI

CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones

TL;DR: CarSafe is the first dual-camera sensing app for smartphones and represents a new disruptive technology because it provides similar advanced safety features otherwise only found in expensive top-end cars.