research-article

Real-time traffic estimation at vehicular edge nodes

Authors:

Marco Gruteser,

Ramesh GovindanAuthors Info & Claims

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

Article No.: 3, Pages 1 - 13

https://doi.org/10.1145/3132211.3134461

Published: 12 October 2017 Publication History

Abstract

Traffic estimation has been a long-studied problem, but prior work has mostly provided coarse estimates over large areas. This work proposes effective fine-grained traffic volume estimation using in-vehicle dashboard mounted cameras. Existing work on traffic estimation relies on static traffic cameras that are usually deployed at crowded intersections and at some traffic lights. For streets with no traffic cameras, some well-known navigation apps (e.g., Google Maps, Waze) are often used to get the traffic information but these applications depend on limited number of GPS traces to estimate speed, and therefore may not show the average speed experienced by every vehicle. Moreover, they do not give any information about the number of vehicles traveling on the road. In this work, we focus on harvesting vehicles as edge compute nodes, focusing on sensing and interpretation of traffic from live video streams. With this goal, we consider a system that uses the dash-cam video collected on a drive, and executes object detection and identification techniques on this data to detect and count vehicles. We use image processing techniques to estimate the lane of traveling and speed of vehicles in real-time. To evaluate this system, we recorded several trips on a major highway and a university road. The results show that vehicle count accuracy depends on traffic conditions heavily but even during the peak hours, we achieve more than 90% counting accuracy for the vehicles traveling in the left most lane. For the detected vehicles, results show that our speed estimation gives less than 10% error across diverse roads and traffic conditions, and over 91% lane estimation accuracy for vehicles traveling in the left most lane (i.e., the passing lane).

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Index Terms

Real-time traffic estimation at vehicular edge nodes
1. Computer systems organization
  1. Real-time systems
    1. Real-time system architecture
2. Information systems
  1. Information systems applications
    1. Mobile information processing systems

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Published In

cover image ACM Conferences

SEC '17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing

October 2017

365 pages

ISBN:9781450350877

DOI:10.1145/3132211

General Chair:
Junshan Zhang
Arizona State University
,
Program Chairs:
Mung Chiang
Purdue University and Princeton Edge Lab
,
Bruce Maggs
Akamai/Duke University

Copyright © 2017 ACM.

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Published: 12 October 2017

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SEC '17: IEEE/ACM Symposium on Edge Computing

October 12 - 14, 2017

California, San Jose

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SEC '17 Paper Acceptance Rate 20 of 41 submissions, 49%;

Overall Acceptance Rate 40 of 100 submissions, 40%

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https://doi.org/10.3390/s23104667
Wang ZGuo BWang QZhang DYu Z(2023)MobiEye: An Efficient Shopping-Assistance System for the Visually Impaired With Mobile Phone SensingIEEE Transactions on Human-Machine Systems10.1109/THMS.2023.330556653:5(865-874)Online publication date: Oct-2023
https://doi.org/10.1109/THMS.2023.3305566
Wasule SKhadatkar GPendke VRane P(2023)Xavier Vision: Pioneering Autonomous Vehicle Perception with YOLO v8 on Jetson Xavier NX2023 IEEE Pune Section International Conference (PuneCon)10.1109/PuneCon58714.2023.10450077(1-6)Online publication date: 14-Dec-2023
https://doi.org/10.1109/PuneCon58714.2023.10450077
Chen CZeng YLi HLiu YWan S(2023)A Multihop Task Offloading Decision Model in MEC-Enabled Internet of VehiclesIEEE Internet of Things Journal10.1109/JIOT.2022.314352910:4(3215-3230)Online publication date: 15-Feb-2023
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Cho HYoon YKim JYeo H(2023)Urban Traffic Density Estimation from Vehicle-mounted Camera for Real-time Application2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)10.1109/ICAIIC57133.2023.10066969(547-552)Online publication date: 20-Feb-2023
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