DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

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 t...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.148163-148175
Main Authors: Chang, Wan-Jung, Chen, Liang-Bi, Su, Ke-Yu
Format: Article
Language:English
Subjects:
Acceleration
Accident detection
Accidents
Advanced driver assistance system (ADAS)
artificial intelligence over Internet of Things (AIoT)
automotive
Cloud computing
Deep learning
Emergency response
head-on and single-vehicle accident detection
Indexes
Internet of Vehicles
Internet of Vehicles (IoV)
Response time
Smart phones
Telematics
Time factors
Traffic accidents
Vehicles
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2946468