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http://dx.doi.org/10.17661/jkiiect.2021.14.2.128

A Design of the Vehicle Crisis Detection System(VCDS) based on vehicle internal and external data and deep learning  

Son, Su-Rak (Catholic Kwandong University, Department of Computer Engineering)
Jeong, Yi-Na (Catholic Kwandong University, Department of Software)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.2, 2021 , pp. 128-133 More about this Journal
Abstract
Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.
Keywords
Autonomous vehicles; Convolutional Neural Networks; You Only Look Once; Crisis Detection; Pedestrian recognition;
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