Global distribution and readiness status of artificial intelligence application on mobility projects by Andante Hadi Pandyaswargo, Meilinda Fitriani Nur Maghfiroh, Hiroshi Onoda

“…Many mobility projects are developing artificial intelligence (AI) technologies to improve the operational efficiency of mobility, such as charging system optimization for electric vehicles (EVs), autonomous driving, and traffic controls. This study presents such projects’ global distribution by showing a geographic information system (GIS)-generated map and analyzes the readiness level of those technologies by employing the Japanese Technology Readiness Assessment (J-TRA) methodology. …”
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What Road Elements are More Important than Others for Safe Driving on Urban Roads? by Hui Xu, Jianping Wu

“…Statistical difference between the experienced and non-experienced drivers recommends that driver assistance system should be sufficiently trained in various conditions, to build up autonomous driving tactics and skills. This research promotes the understanding of driving cognition pattern to provide insights into the development of road digitalization.…”
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Analysis of ADAS Radars with Electronic Warfare Perspective by Alper Cemil, Mehmet Ünlü

“…The increasing demand in the development of autonomous driving systems makes the employment of automotive radars unavoidable. …”
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Estimation of Road Wetness from a Passenger Car by Wiyao Edjeou, Ebrahim Riahi, Manuela Gennesseaux, Veronique Cerezo, Minh-Tan Do

“…Perspectives for driver assistance, or more generally, for autonomous driving to improve traffic safety, are also highlighted.…”
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Computational Resources Allocation and Vehicular Application Offloading in VEC Networks by Fan Gu, Xiaoying Yang, Xianwei Li, Haiquan Deng

“…With the advances in wireless communications and the Internet of Things (IoT), various vehicular applications such as image-aided navigation and autonomous driving are emerging. These vehicular applications require a significant number of computation resources and a lower processing delay. …”
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Survey and Classification of Automotive Security Attacks by Florian Sommer, Jürgen Dürrwang, Reiner Kriesten

“…Due to current development trends in the automotive industry towards stronger connected and autonomous driving, the attack surface of vehicles is growing which increases the risk of security attacks. …”
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A Combined Reinforcement Learning and Model Predictive Control for Car-Following Maneuver of Autonomous Vehicles by Liwen Wang, Shuo Yang, Kang Yuan, Yanjun Huang, Hong Chen

“…Abstract Model predictive control is widely used in the design of autonomous driving algorithms. However, its parameters are sensitive to dynamically varying driving conditions, making it difficult to be implemented into practice. …”
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Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review by Deven Nahata, Kareem Othman

“…This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.…”
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Hololens Used for Precise Position Tracking of the Third Party Devices - Autonomous Vehicles by Jindrich Cyrus, David Krcmarik, Reza Moezzi, Jan Koci, Michal Petru

“…The information is precise enough to use it for reporting the position for the purpose of autonomous driving. Several experiments have been executed in vast areas (20 m x 40 m) in order to find out the potential error coming from vibrations or other effects when moving the HoloLens. …”
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A Collaborative Control Scheme for Smart Vehicles Based on Multi-Agent Deep Reinforcement Learning by Liyan Shi, Hairui Chen

“…With the development of artificial intelligence and autonomous driving technology, the vehicle-road cooperative control system combined with artificial intelligence technology can provide more effective and adaptive traffic control solutions for intelligent transportation systems. …”
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3D point cloud object detection algorithm based on Transformer by LIU Mingyang, YANG Qiming, HU Guanhua, GUO Yan, ZHANG Jiandong

“…This article proposes a Transformer based 3D point cloud object detection algorithm, and combines the characteristics of point clouds in autonomous driving scenarios to propose an improved spatial modulation attention and heat map initialization strategy for training acceleration and query initialization, achieving good detection performance in shallow networks. …”
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SEMANTIC LABELLING OF ROAD FURNITURE IN MOBILE LASER SCANNING DATA by F. Li, S. Oude Elberink, G. Vosselman

“…Road furniture semantic labelling is vital for large scale mapping and autonomous driving systems. Much research has been investigated on road furniture interpretation in both 2D images and 3D point clouds. …”
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Towards the transferable audio adversarial attack via ensemble methods by Feng Guo, Zheng Sun, Yuxuan Chen, Lei Ju

“…Abstract In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. …”
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Research on GNSS INS & GNSS/INS Integrated Navigation Method for Autonomous Vehicles: A Survey by Yuan He, Jichuan Li, Junjie Liu

“…Meanwhile, the continuous development of various emerging technologies in recent years has paved the way for the realization of fully autonomous driving. Furthermore, high-precision navigation and positioning systems are crucial to self-driving cars. …”
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Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review by Faisal Khan, Saqib Salahuddin, Hossein Javidnia

“…Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. …”
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FANet: Improving 3D Object Detection with Position Adaptation by Jian Ye, Fushan Zuo, Yuqing Qian

“…Three-dimensional object detection plays a crucial role in achieving accurate and reliable autonomous driving systems. However, the current state-of-the-art two-stage detectors lack flexibility and have limited feature extraction capabilities to effectively handle the disorder and irregularity of point clouds. …”
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Optimization of AUTOSAR Communication Stack in the Context of Advanced Driver Assistance Systems by Răzvan Bogdan, Mihaela Crișan-Vida, Darius Barmayoun, Loredana Lavinia Staicu, Robert Valentin Puiu, Mădălina Lup, Marius Marcu

“…New trends in the automotive industry such as autonomous driving and Car2X require a large amount of data to be exchanged between different devices. …”
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Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset by Junjie Wu, Wen Liu, Yoshihisa Maruyama

“…Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. …”
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AttentionPose: Attention-driven end-to-end model for precise 6D pose estimation by Rasheed Mayada Abdalsalam, Farhan Rabah Nori, Jasim Wesam M.

“…Addressing the complex problem of 6D pose estimation from single RGB images is essential for robotics, augmented reality, and autonomous driving applications. The aim of this study is to overcome limitations in handling scenes with high object occlusion and clutter. …”
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Radar-Camera Fusion Network for Depth Estimation in Structured Driving Scenes by Shuguang Li, Jiafu Yan, Haoran Chen, Ke Zheng

“…Depth estimation is an important part of the perception system in autonomous driving. Current studies often reconstruct dense depth maps from RGB images and sparse depth maps obtained from other sensors. …”
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