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Article

The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

1
Department of Smart City, Hongik University, Seoul 04066, Korea
2
Research Institute of Science and Technology, Hongik University, Seoul 04066, Korea
3
Department of Urban Planning, Hongik University, Seoul 04066, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Nikolay Hinov
Electronics 2021, 10(14), 1737; https://doi.org/10.3390/electronics10141737
Received: 1 June 2021 / Revised: 8 July 2021 / Accepted: 17 July 2021 / Published: 19 July 2021
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types. View Full-Text
Keywords: automated driving systems; the design of preventive; CNN; vehicle detection; distance estimation automated driving systems; the design of preventive; CNN; vehicle detection; distance estimation
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MDPI and ACS Style

Lee, W.; Kang, M.-H.; Song, J.; Hwang, K. The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network. Electronics 2021, 10, 1737. https://doi.org/10.3390/electronics10141737

AMA Style

Lee W, Kang M-H, Song J, Hwang K. The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network. Electronics. 2021; 10(14):1737. https://doi.org/10.3390/electronics10141737

Chicago/Turabian Style

Lee, Wooseop, Min-Hee Kang, Jaein Song, and Keeyeon Hwang. 2021. "The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network" Electronics 10, no. 14: 1737. https://doi.org/10.3390/electronics10141737

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