Haider, Majumder, Peyal, Mahmudul Kabir, Huang, Tao, and Xiang, Wei (2024) Road crack avoidance: a convolutional neural network-based smart transportation system for intelligent vehicles. Journal of Intelligent Transportation Systems. (In Press)

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Abstract

Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions.

Item ID:	77521
Item Type:	Article (Research - C1)
ISSN:	1547-2442
Keywords:	Road crack; Convolutional neural network; smart transportation; intelligent vehicles
Copyright Information:	© 2023 Taylor & Francis Group, LLC
Date Deposited:	13 Feb 2023 23:18
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460304 Computer vision @ 40% 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 20% 40 ENGINEERING > 4002 Automotive engineering > 400204 Automotive safety engineering @ 40%
SEO Codes:	27 TRANSPORT > 2703 Ground transport > 270311 Road safety @ 40% 27 TRANSPORT > 2703 Ground transport > 270302 Autonomous road vehicles @ 30% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 30%
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