Velocity range-based reward shaping technique for effective map-less navigation with LiDAR sensor and deep reinforcement learning by HyeokSoo Lee, HyeokSoo Lee, Jongpil Jeong

“…For this purpose, we reviewed the hardware configuration of mobile robots capable of autonomous driving, checked the characteristics of the main core sensors, and investigated the core technologies of autonomous driving. …”
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Enhancing Rainy Weather Driving: Deep Unfolding Network With PGD Algorithm for Single Image Deraining by Chao Hu, Huiwei Wang

“…In addition, we combined high-level perception tasks, i.e., target detection and semantic segmentation, for autonomous driving to compare the perceptual effectiveness of autonomous driving before and after removing the rain. …”
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HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology by Vemparala, Manoj-Rohit, Fasfous, Nael, Frickenstein, Alexander, Valpreda, Emanuele, Camalleri, Manfredi, Zhao, Qi, Unger, Christian, Nagaraja, Naveen-Shankar, Martina, Maurizio, Stechele, Walter

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A Convolutional Neural Network-Based End-to-End Self-Driving Using LiDAR and Camera Fusion: Analysis Perspectives in a Real-World Environment by Mingyu Park, Hyeonseok Kim, Seongkeun Park

“…In this paper, we develop end-to-end autonomous driving based on a 2D LiDAR sensor and camera sensor that predict the control value of the vehicle from the input data, instead of modeling rule-based autonomous driving. …”
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A Precrash Scenario Analysis Comparing Safety Performance across Autonomous Vehicle Driving Modes by Tao Wang, Juncong Chen, Wenyong Li, Jun Chen, Xiaofei Ye

“…Precrash scenario analysis for autonomous vehicles (AVs) is critical for improving the safety of autonomous driving, yet the scenario differences between different driving modes are unexplored. …”
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Deep Reinforcement Learning For Control by Bakar, Nurul Asyikin Abu

“…Furthermore, autonomous driving is difficult in terms of the outcomes and safety judgments that must be taken. …”
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Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario by Seungwoo Nham, Jinho Lee, Seongryul Yang, Jihun Kim, Shunsuke Kamijo

“…The developments presented signal substantial progress in the field of autonomous driving technology.…”
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Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles by Rui Zhao, Kui Wang, Yang Xiao, Fei Gao, Zhenhai Gao

“…With the recent advancements in machine learning technology, the accuracy of autonomous driving object detection models has significantly improved. …”
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Dual channel transmission for reliable V2X broadcasting messages by Hyun Seo Oh, Do Wook Kang, Yoo Seung Song

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HW-Flow-Fusion: Inter-Layer Scheduling for Convolutional Neural Network Accelerators with Dataflow Architectures by Emanuele Valpreda, Pierpaolo Morì, Nael Fasfous, Manoj Rohit Vemparala, Alexander Frickenstein, Lukas Frickenstein, Walter Stechele, Claudio Passerone, Guido Masera, Maurizio Martina

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View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles by Melese Ayalew, Shijie Zhou, Imran Memon, Md Belal Bin Heyat, Faijan Akhtar, Xiaojuan Zhang

“…Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. …”
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Implementation of a Lightweight Semantic Segmentation Algorithm in Road Obstacle Detection by Bushi Liu, Yongbo Lv, Yang Gu, Wanjun Lv

“…Considering the needs of autonomous driving and assisted driving, in a general way, computer vision technology is used to find obstacles to avoid collisions, which has made semantic segmentation a research priority in recent years. …”
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Simulation based perception testing for autonomous vehicles by Yang, Xuehuan

“…Perception Testing technologies are widely applied in various scenarios, like industrial and academic research applications for autonomous driving systems. Accurate and robust autonomous driving simulation perception is pivotal for safety-guidance autonomous vehicles (AV). …”
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Implementation of traffic sign recognition on the scaled vehicle model by Mitrović Miloš, Popović Vladimir, Stamenković Dragan

“…The most accessible way for students of mechanical and software engineers to learn about autonomous vehicles is by applying algorithms and systems necessary for autonomous driving on the scaled vehicle model. These models are, as in this case, and are equipped with all systems necessary for autonomous driving, such as a four-wheel drive powertrain, a suspension system, an electrically controlled steering system, a brain-computer and a camera. …”
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A Lateral Control Method of Intelligent Vehicles Based on Shared Control by Gang Li, Pengfei Shang, Changbing Zheng, Dehui Sun

“…For the vehicle dynamic model with two-degrees of freedom, an autonomous driving controller based on LQR and a driver’s intention-based fuzzy controller are designed. …”
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A Study on Data Analysis for Improving Driving Safety in Field Operational Test (FOT) of Autonomous Vehicles by Seok-San Shin, Ho-Joon Kang, Seong-Jin Kwon

“…In this study, an autonomous driving test was conducted from the perspective of FOT (field operational test). …”
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Performance Evaluation of Zone-Based In-Vehicle Network Architecture for Autonomous Vehicles by Chulsun Park, Sungkwon Park

“…Among the various functions, autonomous driving-related technologies are being added to all vehicles, from low options to high options. …”
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A self-driving solution for resource-constrained autonomous vehicles in parked areas by Jin Qian, Liang Zhang, Qiwei Huang, Xinyi Liu, Xiaoshuang Xing, Xuehan Li

“…This paper proposes an efficient autonomous driving solution based on path planning, target recognition, and driving decision-making as its core components. …”
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Research on the Optimal Machine Learning Classifier for Traffic Signs by Wang Boyu

“…Now autonomous driving is a hot topic, and the identification of traffic signs is also extremely important for autonomous driving. …”
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Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm by Zhenhai Gao, Tong Yu, Tianjun Sun, Haoyuan Zhao

“…Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. …”
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