通过协同驾驶实现更高级别的车辆自动化:从规划和控制角度看路线图

IF 14 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-08 DOI:10.1109/TIV.2024.3363873
Haoran Wang;Yongwei Feng;Yonglin Tian;Ziran Wang;Jia Hu;Masayoshi Tomizuka
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引用次数: 0

摘要

在不断发展的车辆自动化领域,协同自动驾驶(CDA)站在了最前沿,提升了在错综复杂的现实环境中的驾驶能力。本研究旨在从规划与控制(PnC)的角度进行深入研究,为 CDA 的未来发展指明方向。它根据汽车工程师学会(SAE)定义的 CDA 类别对最新文献进行了分类。分析了每个 CDA 类别的优势、劣势和对 PnC 的要求。这种分析有助于确定需要改进的领域,并为潜在的研究方向提供见解。研究进一步讨论了 CDA 的发展方向,为进一步加强和丰富 CDA 研究的潜在领域提供了宝贵的见解。建议的领域包括抗干扰的控制鲁棒性;互联与自动驾驶车辆(CAV)和人类驾驶车辆(HV)混合环境中的风险意识规划;用于增强协调的车辆信号耦合建模;车辆分组以增强排队的机动性。
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Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective
Cooperative Driving Automation (CDA) stands at the forefront of the evolving landscape of vehicle automation, elevating driving capabilities within intricate real-world environments. This research aims to navigate the path toward the future of CDA by offering a thorough examination from the perspective of Planning and Control (PnC). It classifies state-of-the-art literature according to the CDA classes defined by the Society of Automotive Engineers (SAE). The strengths, weaknesses, and requirements of PnC for each CDA class are analyzed. This analysis helps identify areas that need improvement and provides insights into potential research directions. The research further discusses the evolution directions for CDA, providing valuable insights into the potential areas for further enhancement and enrichment of CDA research. The suggested areas include: Control robustness against disturbance; Risk-aware planning in a mixed environment of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs); Vehicle-signal coupled modeling for coordination enhancement; Vehicle grouping to enhance the mobility of platooning.
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来源期刊
IEEE Transactions on Intelligent Vehicles
IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization
CiteScore
12.10
自引率
13.40%
发文量
177
期刊介绍: The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.
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