Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
arXiv:2305.11239 · doi:10.1109/TSMC.2023.3276218
Abstract
Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
18 pages, 4 figures, 3 tables, in IEEE Trans. Syst. Man Cybern. Syst
References in corpus (6)
- Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
- Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys
- A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development
- Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
- Survey on Congestion Detection and Control in Connected Vehicles
- Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches