A deep learning platooning-based video information-sharing Internet of Things framework for autonomous driving systems by Zishuo Zhou, Zahid Akhtar, Ka Lok Man, Kamran Siddique

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TF-YOLO: A Transformer–Fusion-Based YOLO Detector for Multimodal Pedestrian Detection in Autonomous Driving Scenes by Yunfan Chen, Jinxing Ye, Xiangkui Wan

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HDM-RRT: A Fast HD-Map-Guided Motion Planning Algorithm for Autonomous Driving in the Campus Environment by Xiaomin Guo, Yongxing Cao, Jian Zhou, Yuanxian Huang, Bijun Li

Subjects: “…autonomous driving…”
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Autonomous Driving Robot That Drives and Returns along a Planned Route in Underground Mines by Recognizing Road Signs by Heonmoo Kim, Yosoon Choi

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Continual Learning Strategy in One-Stage Object Detection Framework Based on Experience Replay for Autonomous Driving Vehicle by Jeng-Lun Shieh, Qazi Mazhar ul Haq, Muhamad Amirul Haq, Said Karam, Peter Chondro, De-Qin Gao, Shanq-Jang Ruan

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A Robust Registration Method for Autonomous Driving Pose Estimation in Urban Dynamic Environment Using LiDAR by Rendong Wang, Youchun Xu, Miguel Angel Sotelo, Yulin Ma, Thompson Sarkodie-Gyan, Zhixiong Li, Weihua Li

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A Safe and Efficient Lane Change Decision-Making Strategy of Autonomous Driving Based on Deep Reinforcement Learning by Kexuan Lv, Xiaofei Pei, Ci Chen, Jie Xu

Subjects: “…autonomous driving…”
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Integrating Modular Pipelines with End-to-End Learning: A Hybrid Approach for Robust and Reliable Autonomous Driving Systems by Luis Alberto Rosero, Iago Pachêco Gomes, Júnior Anderson Rodrigues da Silva, Carlos André Przewodowski, Denis Fernando Wolf, Fernando Santos Osório

Subjects: “…autonomous driving…”
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Towards autonomous driving : review and perspectives on configuration and control of four-wheel independent drive/steering electric vehicles by Hang, Peng, Chen, Xinbo

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AutoDRIVE: a comprehensive, flexible and integrated digital twin ecosystem for enhancing autonomous driving research and education by Samak, Tanmay, Samak, Chinmay, Kandhasamy, Sivanathan, Krovi, Venkat, Xie, Ming

“…This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. …”
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Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations by He, Xiangkun, Huang, Wenhui, Lv, Chen

“…Despite the substantial advancements in reinforcement learning (RL) in recent years, ensuring trustworthiness remains a formidable challenge when applying this technology to safety-critical autonomous driving domains. One pivotal bottleneck is that well-trained driving policy models may be particularly vulnerable to observational perturbations or perceptual uncertainties, potentially leading to severe failures. …”
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Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving by Huang Hsiang, Yung-Yuan Chen

Subjects: “…autonomous driving…”
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Adaptive multi-modal interface model concerning mental workload in take-over request during semi-autonomous driving by Weiya Chen, Tetsuo Sawaragi, Toshihiro Hiraoka

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Instance Segmentation Frustum–PointPillars: A Lightweight Fusion Algorithm for Camera–LiDAR Perception in Autonomous Driving by Yongsheng Wang, Xiaobo Han, Xiaoxu Wei, Jie Luo

“…The fusion of camera and LiDAR perception has become a research focal point in the autonomous driving field. Existing image–point cloud fusion algorithms are overly complex, and processing large amounts of 3D LiDAR point cloud data requires high computational power, which poses challenges for practical applications. …”
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The effect of a startle-based warning, age, sex, and secondary task on takeover actions in critical autonomous driving scenarios by M. Griffith, R. Akkem, R. Akkem, J. Maheshwari, T. Seacrist, K. B. Arbogast, K. B. Arbogast, V. Graci, V. Graci

“…Introduction: In highly autonomous driving scenarios, it is critical to identify strategies to accelerate reaction times since drivers may take too long to take over control of the vehicle. …”
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A Two-Layer Model Predictive Path-Tracking Control With Curvature Adaptive Method for High-Speed Autonomous Driving by Hebing Liu, Jinhong Sun, Ka Wai Eric Cheng

“…A two-layer model predictive control (MPC) algorithm with curvature adaptive is introduced and adopted in path tracking, especially for high-speed autonomous driving. Whether the vehicle can stably reach a safe driving speed in advance is the main consideration of rollover and speed-overshoot avoidance, especially when the trajectory with large curvatures. …”
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Weakly-Supervised Recommended Traversable Area Segmentation Using Automatically Labeled Images for Autonomous Driving in Pedestrian Environment with No Edges by Yuya Onozuka, Ryosuke Matsumi, Motoki Shino

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PVI-Net: Point–Voxel–Image Fusion for Semantic Segmentation of Point Clouds in Large-Scale Autonomous Driving Scenarios by Zongshun Wang, Ce Li, Jialin Ma, Zhiqiang Feng, Limei Xiao

“…<i>In this study, we introduce a novel framework for the semantic segmentation of point clouds in autonomous driving scenarios, termed PVI-Net.</i> This framework uniquely integrates three different data perspectives—point clouds, voxels, and distance maps—executing feature extraction through three parallel branches. …”
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WHUVID: A Large-Scale Stereo-IMU Dataset for Visual-Inertial Odometry and Autonomous Driving in Chinese Urban Scenarios by Tianyang Chen, Fangling Pu, Hongjia Chen, Zhihong Liu

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Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving by Yingtong Zhou, Tiansi Han, Qiong Nie, Yuxuan Zhu, Minghu Li, Ning Bian, Zhiheng Li

“…Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. …”
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