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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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Proceedings ArticleDOI

The pothole patrol: using a mobile sensor network for road surface monitoring

TL;DR: This paper describes a system and associated algorithms to monitor this important civil infrastructure using a collection of sensor-equipped vehicles, which they call the Pothole Patrol (P2), which uses the inherent mobility of the participating vehicles, opportunistically gathering data from vibration and GPS sensors, and processing the data to assess road surface conditions.
Proceedings ArticleDOI

Driving style recognition using a smartphone as a sensor platform

TL;DR: A novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion to detect, recognize and record potentially-aggressive driving actions without external processing and utilizes Euler representation of device attitude to aid in classification.
Journal ArticleDOI

A Review of Computer Vision Techniques for the Analysis of Urban Traffic

TL;DR: A comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions is presented.
Proceedings ArticleDOI

Demo: how long to wait?: predicting bus arrival time with mobile phone based participatory sensing

TL;DR: A bus arrival time prediction system based on bus passengers' participatory sensing that achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions and is more generally available and energy friendly.
Proceedings ArticleDOI

Demo: SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory

TL;DR: SignalGuru as discussed by the authors is a software service that relies solely on collaborating windshield-mounted mobile phones to provide information about the schedule of traffic signals and enable a set of novel driver-assistance applications.