A vehicular ad hoc network (VANET) is essential for the autonomous vehicle industry, and with the advancement in VANET technology, security threats are increasing rapidly. Mitigation of these threats needs an intelligent security protocol that provides unbreakable security. In recent times, various three-factor authentication solutions for VANET were introduced that adopt the centralized Trusted Authority T A , which is responsible for assigning authentication parameters during vehicle registration, and the authentication process depends on these parameters. This article first explains the vulnerabilities of the recent three-factor (3F) authentication scheme presented by Xu et al. Our analysis proves that if an RSU is dishonest, it can easily bypass the T A and can create a session with OBU . Furthermore, this paper puts forward a new scheme that provides the 3F authentication for VANETs (TFPPASV) to resist RSU from bypassing the T A and to offer user privacy. The proposed scheme fulfills the security and performance requirements of the VANET. We use BAN-Logic analysis to perform a formal security analysis of the proposed scheme, in addition to the informal security feature discussion. Finally, we compare the security and performance of the proposed TFPPASV with some recent and related schemes.

Crack inspections of automotive engine components are usually conducted manually; this is often tedious, with a high degree of subjectivity and cost. Therefore, establishing a robust and efficient method will improve the accuracy and minimize the subjectivity of the inspection. This paper presents a robust approach towards crack classification, using transfer learning and fine-tuning to train a pre-trained ConvNet model. Two deep convolutional neural network (DCNN) approaches to training a crack classifier—namely, via (1) a Light ConvNet architecture from scratch, and (2) fined-tuned and transfer learning top layers of the ConvNet architectures of AlexNet, InceptionV3, and MobileNet—are investigated. Data augmentation was utilized to minimize over-fitting caused by an imbalanced and inadequate training sample. Data augmentation improved the accuracy index by 4%, 5%, 7%, and 4%, respectively, for the proposed four approaches. The transfer learning and fine-tuning approach achieved better recall and precision scores. The transfer learning approach using the fine-tuned features of MobileNet attained better classification accuracy and is thus proposed for the training of crack classifiers. Moreover, we employed an up-to-date YOLOv5s object detector with transfer learning to detect the crack region. We obtained a mean average precision (mAP) of 91.20% on the validation set, indicating that the model effectively distinguished diverse engine part cracks.

Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

The advancements in Vehicular Ad Hoc Networks (VANETs) require more intelligent security protocols that ultimately provide unbreakable security to vehicles and other components of VANETs. VANETs face various types of security pitfalls due to the openness characteristics of the VANET communication infrastructure. Researchers have recently proposed different mutual authentication schemes that address security and privacy issues in vehicle-to-vehicle (V2V) communication. However, some V2V security schemes suffer from inadequate design and are hard to implement practically. In addition, some schemes face vehicle traceability and lack anonymity. Hence, this paper’s primary goal is to enhance privacy preservation through mutual authentication and to achieve better security and performance. Therefore, this article first describes the vulnerabilities of a very recent authentication scheme presented by Vasudev et al. Our analysis proves that the design of Vasudev et al.’s scheme is incorrect, and resultantly, the scheme does not provide mutual authentication between a vehicle and vehicle server when multiple vehicles are registered with the vehicle sever. Furthermore, this paper proposes a secure message transmission scheme for V2V in VANETs. The proposed scheme fulfills the security and performance requirements of VANETs. The security analysis of the proposed scheme using formal BAN and informal discussion on security features confirm that the proposed scheme fulfills the security requirements, and the performance comparisons show that the proposed scheme copes with the lightweightness requirements of VANETs.

Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and assess these designs. In this paper, we show that the Yolov4-tiny architecture outperforms the Yolov4, and Yolov5s in terms of mAP@50 and computing time per image.