Time-Series-Based Personalized Lane-Changing Decision-Making Model by Ming Ye, Lei Pu, Pan Li, Xiangwei Lu, Yonggang Liu

“…In recent years, autonomous driving technology has been changing from “human adapting to vehicle” to “vehicle adapting to human”. …”
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Fast Online Coordinate Correction of a Multi-Sensor for Object Identification in Autonomous Vehicles by Wooyoung Lee, Minchul Lee, Myoungho Sunwoo, Kichun Jo

“…We conclude that the proposed method provides sufficient correcting performance for autonomous driving applications when the multi-sensor coordinates are mismatched.…”
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Driving into the future: a cross-cutting analysis of distributed artificial intelligence, CCAM and the platform economy by Marc Guerreiro Augusto, Benjamin Acar, Andrea Carolina Soto, Fikret Sivrikaya, Sahin Albayrak

“…It requires a comprehensive stack of embedded software components, enabled by open-source and proprietary platforms at different abstraction layers, and then operating within a larger ecosystem. Autonomous driving demands connectivity, cooperation and automation to form the cornerstone of autonomous mobility solutions. …”
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Autonomous Vehicle Navigation in Rural Environments Without Detailed Prior Maps by Ort, Teddy, Paull, Liam, Rus, Daniela

“…State-of-the-art autonomous driving systems rely heavily on detailed and highly accurate prior maps. …”
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Object depth estimation from single image by Long, Zhongtian

“…Therefore, depth estimation is an important visual task in autonomous driving. The method of monocular[2] depth estimation is to estimate the depth from a single or a series of visible light photos taken simultaneously in the same scene. …”
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Numerical analysis of beam steering for Si-photonics-LiDAR by Chen, Yiru

“…This technology has a wide application potential in modern autonomous driving, robot navigation, and UAV obstacle avoidance. …”
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Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication by Seong Kyung Kwon, Ji Hwan Seo, Jun Young Yun, Kyoung-Dae Kim

“…Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. …”
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Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution by Malhar Khan, Muhammad Amir Raza, Ghulam Abbas, Salwa Othmen, Amr Yousef, Amr Yousef, Touqeer Ahmed Jumani

“…Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. …”
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Vehicle Following Control via V2V SIMO Communications Using MBD Approach by Jerawat Sopajarn, Apidet Booranawong, Surachate Chumpol, Nattha Jindapetch

“…This reveals that the proposed method can effectively model the vehicle following control and autonomous driving scenario including the effects of V2V communications efficiencies.…”
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The impact of human-AI collaboration types on consumer evaluation and usage intention: a perspective of responsibility attribution by Beibei Yue, Hu Li

“…However, under negative outcome expectations, consumers rate autonomous driving capabilities lower compared to emergency evasion capabilities. …”
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An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis by Tao Xu, Andrei Dragomir, Xucheng Liu, Haojun Yin, Feng Wan, Anastasios Bezerianos, Hongtao Wang

“…The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. …”
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LLM Multimodal Traffic Accident Forecasting by I. de Zarzà, J. de Curtò, Gemma Roig, Carlos T. Calafate

“…We introduce the idea of using real-time interventions with large language models (LLMs) in autonomous driving with the use of lightweight compact LLMs like LLaMA-2 and Zephyr-7b-<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>. …”
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LWD-3D: Lightweight Detector Based on Self-Attention for 3D Object Detection by Shuo Yang, Huimin Lu, Tohru Kamiya, Yoshihisa Nakatoh, Seiichi Serikawa

“…Lightweight modules play a key role in 3D object detection tasks for autonomous driving, which are necessary for the application of 3D object detectors. …”
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AI on the Road: NVIDIA Jetson Nano-Powered Computer Vision-Based System for Real-Time Pedestrian and Priority Sign Detection by Kornel Sarvajcz, Laszlo Ari, Jozsef Menyhart

“…Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. …”
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An Open-Source Low-Cost Mobile Robot System With an RGB-D Camera and Efficient Real-Time Navigation Algorithm by Taekyung Kim, Seunghyun Lim, Gwanjun Shin, Geonhee Sim, Dongwon Yun

“…In addition, we demonstrate the applicability of our mobile robot system through successful autonomous driving in an indoor environment.…”
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AN APPROACH OF HIGH DEFINITION MAP INFORMATION INTERACTION by Y. J. Zhang, W. Huang, W. Huang, W. Huang

“…High definition (HD) maps play a very important role in the realization of autonomous driving technology. It assists self-driving vehicles to efficiently and safely complete a series of driving decisions and route planning by virtue of having most of the accurate and reliable prior information in the road environment. …”
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Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments by Yuhan Xiao, Yufei Liu, Kai Luan, Yuwei Cheng, Xieyuanli Chen, Huimin Lu

“…Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. …”
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Driving Robot for Reproducible Testing: A Novel Combination of Pedal and Steering Robot on a Steerable Vehicle Test Bench by Philip Rautenberg, Clemens Kurz, Martin Gießler, Frank Gauterin

“…This results from its significant influence on energy consumption and Autonomous Driving functions with lateral guidance, such as lane departure and evasion assistance. …”
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A general, context-aware pedestrian trajectory prediction model by Jaipuria, Nikita

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TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs by Tang, Haotian, Yang, Shang, Liu, Zhijian, Hong, Ke, Yu, Zhongming, Li, Xiuyu, Dai, Guohao, Wang, Yu, Han, Song

“…Sparse convolution plays a pivotal role in emerging workloads, including point cloud processing in AR/VR, autonomous driving, and graph understanding in recommendation systems. …”
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