Browsing by Author "Li, Li"

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    Milestones in autonomous driving and intelligent vehicles - Part 1: control, computing system design, communication, HD map, testing, and human behaviors
    (IEEE, 2023-05-29) Chen, Long; Li, Yuchen; Huang, Chao; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue
    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 three 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 1 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 2 for this technical survey) is to review the perception and planning sections. The objective of this article 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 2, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
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    Milestones in autonomous driving and intelligent vehicles: survey of surveys
    (IEEE, 2022-11-24) Chen, Long; Li, Yuchen; Huang, Chao; Li, Bai; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Na, Xiaoxiang; Li, Zixuan; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue
    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, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.
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    Parallel driving in CPSS: a unified approach for transport automation and vehicle intelligence
    (IEEE, 2017-09-15) Wang, Fei-Yue; Zheng, Nan-Ning; Cao, Dongpu; Martinez, Clara Marina; Li, Li; Liu, Teng
    The emerging development of connected and automated vehicles imposes a significant challenge on current vehicle control and transportation systems. This paper proposes a novel unified approach, Parallel Driving, a cloud-based cyberphysical-social systems U+0028 CPSS U+0029 framework aiming at synergizing connected automated driving. This study first introduces the CPSS and ACP-based intelligent machine systems. Then the parallel driving is proposed in the cyber-physical-social space, considering interactions among vehicles, human drivers, and information. Within the framework, parallel testing, parallel learning and parallel reinforcement learning are developed and concisely reviewed. Development on intelligent horizon U+0028 iHorizon U+0028 and its applications are also presented towards parallel horizon. The proposed parallel driving offers an ample solution for achieving a smooth, safe and efficient cooperation among connected automated vehicles with different levels of automation in future road transportation systems.
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    Retrieving common discretionary lane changing characteristics from trajectories
    (IEEE, 2017-11-09) Li, Li; Lv, Chen; Cao, Dongpu; Zhang, Jiajie
    Conventional lane change methods directly collected steering angle data via onboard sensors to accurately capture the actions of individual drivers. We can hardly use such methods to collect massive data from examinees, because of time and financial costs. In order to retrieve common steering behaviors for lots of drivers, we propose a method to retrieve common Discretionary Lane Change (DLC) steering characteristics from trajectory data. The key technique of this new method is solving an inverse problem that converts the measured trajectory into the unmeasured steering maneuvers under the assumed vehicle movement dynamics. We find that most normal DLC trajectories in the Next Generation Simulation (NGSIM) datasets could be well reproduced by a simple target heading angle preview control model. This finding sheds important light into driver behavior study and better explains how human control vehicles. Based on these findings, we can non-intrusively evaluate driving performance or physiological states of drivers based on online roadside monitoring data (e.g. the data collected from roadsidevideo cameras). This opens a promising field of applications for enhancing driving safety.