Paper Info
Title
DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification
Abstract
Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96 and that the average response time for emergency-related announcements is approximately 7 s.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2946468
IEEE ACCESS
Keywords
DocType
Volume
Accidents,Smart phones,Acceleration,Indexes,Vehicles,Time factors,Cloud computing,Advanced driver assistance system (ADAS),artificial intelligence over Internet of Things (AIoT),automotive,deep learning,Internet of Vehicles (IoV),head-on and single-vehicle accident detection
Journal
7
ISSN
Citations 
PageRank 
2169-3536
1
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Wan-Jung Chang11312.53
Liang-Bi Chen22618.40
Ke-Yu Su311.03