University of Oulu

A. Alhilal, T. Braud, X. Sut, L. A. Asadi and P. Hui, "CAD3: Edge-facilitated Real-time Collaborative Abnormal Driving Distributed Detection," 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 2021, pp. 718-728, doi: 10.1109/ICDCS51616.2021.00074

CAD3 : edge-facilitated real-time collaborative abnormal driving distributed detection

Saved in:
Author: Alhilal, Ahmad1; Braud, Tristan1; Su, Xiang2,3;
Organizations: 1The Hong Kong University of Science and Technology, Hong Kong
2University of Helsinki, Helsinki, Finland
3University of Oulu, Oulu, Finland
4AlBaraka Bank Syria, Damascus, Syria
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 13 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-11-02


Speeding, slowing down, and sudden acceleration are the leading causes of fatal accidents on highways. Anomalous driving behavior detection can improve road safety by informing drivers who are in the vicinity of dangerous vehicles. However, detecting abnormal driving behavior at the city-scale in a centralized fashion results in considerable network and computation load, that would significantly restrict the scalability of the system. In this paper, we propose CAD3, a distributed collaborative system for road-aware and driver-aware anomaly driving detection. CAD3 considers a decentralized deployment of edge computation nodes on the roadside and combines collaborative and context-aware computation with low-latency communication to detect and inform nearby drivers of unsafe behaviors of other vehicles in real-time. Adjacent edge nodes collaborate to improve the detection of abnormal driving behavior at the city-scale. We evaluate CAD3 with a physical testbed implementation. We emulate realistic driving scenarios from a real driving data set of 3,000 vehicles, 214,000 trips, and 18 million trajectories of private cars in Shenzhen, China. At the microscopic (road) level, CAD3 significantly improves the accuracy of detection and lowers the number of potential accidents caused by false negatives up to four times and 24 times as compared to distributed standalone and centralized models, respectively. CAD3 can scale up to 256 vehicles connected to a single node while keeping the end-to-end latency under 50 ms and a required bandwidth below 5 mbps. At the mesoscopic (driver-trip) level, CAD3 performs stable and accurate detection over time, owing to local RSU interaction. With a dense deployment of edge nodes, CAD3 can scale up to the size of Shenzhen, a megalopolis of 12 million inhabitant with over 2 million concurrent vehicles at peak hours.

see all

Series: Proceedings of the International Conference on Distributed Computing Systems
ISSN: 1063-6927
ISSN-E: 2575-8411
ISSN-L: 1063-6927
ISBN: 978-1-6654-4513-9
ISBN Print: 978-1-6654-4514-6
Pages: 718 - 728
DOI: 10.1109/ICDCS51616.2021.00074
Host publication: 41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021, 7-10 July 2021, DC, USA
Conference: IEEE International Conference on Distributed Computing Systems
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This research has been supported in part by project 16214817 from the Research Grants Council of Hong Kong, and the 5GEAR and FIT projects from Academy of Finland.
Copyright information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.