DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification
DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification
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DOI：
10.1109/access.2019.2946468
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Su, Ke-Yu
中科院分区：
文献类型：
--
作者：
Chang, Wan-Jung;Chen, Liang-Bi;Su, Ke-Yu
大多数发生交通事故的人都会得到司机、乘客或其他人的帮助。然而，当交通事故发生在人烟稀少的地区，或者车内只有驾驶员一人且事故导致意识丧失时，在黄金窗口内就没有人可以向有关部门发送求救信息以求救。考虑到这些问题，需要一种检测高速正面碰撞和单车碰撞、分析情况并发出警报的方法。为了解决这些问题，本文提出了一种名为 DeepCrash 的基于深度学习的车联网 (IoV) 系统，该系统包括带有车辆自碰撞检测传感器和前置摄像头的车载信息娱乐 (IVI) 远程信息处理平台、基于云的深度学习服务器和基于云的管理平台。当检测到正面或单车碰撞时，事故检测信息上传至云端数据库服务器，进行自碰撞车辆事故识别，并提供相关紧急通知。实验结果表明，交通碰撞检测准确率可达96级，紧急公告的平均响应时间约为7 s。
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.