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Journal ArticleDOI

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

TLDR
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.
Abstract
In this paper we present a smartphone-based real-time video overtaking architecture for vehicular networks. The developed application 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. Under such conditions, the driver does not have a clear view of the road ahead and of any vehicles that might be approaching from the opposite direction, resulting in a high probability of accident occurrence. Our application relies on the use of a dashboard-mounted smartphone with the back camera facing the windshield, and having the screen towards the driver. A video is streamed from the vehicle ahead to the vehicle behind automatically, where it is displayed so that the driver can decide if it is safe to overtake. One of the major challenges is the way to pick the right video source and destination among vehicles in close proximity, depending on their relative position on the road. For this purpose, we have focused on two different methods: one relying solely on GPS data, and the other involving the use of the camera and vehicle heading information. Our experiments show that the faster method, using just the location information, is prone to errors due to GPS inaccuracies. A second method that depends on data fusion from the optical sensor and GPS, although accurate over short distances, becomes more computationally intensive, and its performance significantly depends on the quality of the camera.

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Citations
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Journal ArticleDOI

UWB Based Relative Planar Localization with Enhanced Precision for Intelligent Vehicles

TL;DR: Simulations and experiments are carried out to show that the presented algorithm significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.
Journal ArticleDOI

Vehicle Reidentification via Multifeature Hypergraph Fusion

Abstract: Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.
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

Driver Distraction Recognition Based on Smartphone Sensor Data

TL;DR: This study develops a smartphone sensor based driver distraction system using an ensemble learning method, and shows that the best weighted F1-score of the proposed system is 87% with all smartphone sensor signals.
Journal ArticleDOI

FullStop: A Camera-Assisted System for Characterizing Unsafe Bus Stopping

TL;DR: 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.
Proceedings ArticleDOI

Approaching Rutted Road-Segment Alert using Smartphone

TL;DR: This work presents an alerting system that detects and localizes road ruts in order to release a prior-rut notification to the driver using no additional devices but his smartphone.
Proceedings ArticleDOI

A Smartphone-Based Probe Data Platform for Road Management and Safety in Developing Countries

TL;DR: A probe data platform for sensing, detecting and visualizing road roughness and driving behavior using smartphones and cloud computing is proposed for better road planning, management and safety through distributed data collection and data analytics.
Journal ArticleDOI

Integration of vehicular network and smartphones to provide real-time visual assistance during overtaking:

TL;DR: An affordable Intelligent Transportation Systems that make use of standard smartphones to assist drivers when overtaking that autonomously creates a network among the close-by vehicles and provides drivers with a real-time video feed from the one located just ahead.
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