Article
Engineering, Civil
Xuting Duan, Chen Sun, Daxin Tian, Jianshan Zhou, Dongpu Cao
Summary: This paper proposes an optimal control framework for cooperative lane-change motion planning in multi-vehicle scenarios. The framework considers the reconfiguration and shape maintenance of platoons, and utilizes geometric contour models and collision avoidance constraints to model the cooperative tasks. Simulation and contrast experiments are conducted, and the results verify the reasonability, effectiveness, and unification of the proposed framework.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Mechanical
Yang Ding, Weichao Zhuang, Liangmo Wang, Jingxing Liu, Levent Guvenc, Zhen Li
Summary: This paper proposes an integrated lane-change trajectory planning method for advanced driver assistance system of connected and automated vehicles. The method combines a time-based lane-change model and constraints induced by surrounding vehicles to achieve safer and more comfortable lane changes.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Venkata Karteek Yanumula, Panagiotis Typaldos, Dimitrios Troullinos, Milad Malekzadeh, Ioannis Papamichail, Markos Papageorgiou
Summary: The paper proposes a movement strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment by using an optimal control approach. State-dependent constraints and an objective function are considered to ensure safe and efficient vehicle trajectories. The feasibility of the approach is demonstrated through simulations on a lane-free ring-road. The proposed method shows high efficiency in delivering desirable outcomes for CAV traffic.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Transportation Science & Technology
Handong Yao, Xiaopeng Li
Summary: This study focuses on trajectory smoothing for controlling CAVs in mixed traffic to reduce traffic oscillations, proposing a model that considers lane-change awareness and having superior performance in numerical experiments, providing additional benefits in overall system performance.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Chemistry, Multidisciplinary
Senlin Zhang, Guohong Deng, Echuan Yang, Jian Ou
Summary: This paper focuses on researching a trajectory planning strategy for autonomous lane changing of intelligent vehicles in urban multi-vehicle traffic environments. The study analyzed and compared three mathematical models for lane changing trajectories, and identified that the fifth polynomial is the most suitable. A collision avoidance algorithm was proposed to eliminate unsafe trajectories. The TOPSIS algorithm was used to solve the multi-objective optimization problem and obtain the optimal expected trajectory. Simulation results showed improved lane-changing efficiency and no collisions. In general, the proposed method achieves safe, efficient and stable lane changing.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Electrical & Electronic
Shuo Yang, Hongyu Zheng, Junmin Wang, Abdelkader El Kamel
Summary: This article introduces a human-like automated lane-changing system that takes into account personalized factors and traffic environmental factors to derive a personalized human-like lane-changing trajectory planning model. The combination of longitudinal and lateral models in the system aims to achieve a more realistic lane-changing operation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Review
Engineering, Civil
Qianwen Li, Zhiwei Chen, Xiaopeng Li
Summary: This study provides an overview of theoretical models and field experiments of CAV platoon merging and splitting operations. A three-step framework is proposed to unify existing representative studies. This study contributes to the literature by providing a framework that categorizes relevant literature and guides the successful development of platoon merging and splitting operations. It offers researchers and practitioners a rich reference for further investigations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation Science & Technology
Panagiotis Typaldos, Markos Papageorgiou, Ioannis Papamichail
Summary: This article presents a path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways. The algorithm utilizes real-time information exchange and short-term prediction to improve the efficiency of connected controlled vehicles in achieving their desired speed and improving the overall traffic flow.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Jieming Chen, Yue Zhou, Edward Chung
Summary: In this paper, a mixed integer nonlinear programming (MINLP) model is proposed and solved to improve the traffic efficiency and safety at freeway on-ramp merging areas. The proposed method optimizes multiple vehicles' trajectories and their merging sequence to cooperatively minimize disruption from ramps. The evaluation results show that the proposed method outperforms benchmark CAV control algorithms and has promising computational efficiency for real-time merging tasks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Huile Xu, Wei Xiao, Christos G. Cassandras, Yi Zhang, Li Li
Summary: This paper addresses the problem of safely controlling Connected and Automated Vehicles (CAVs) crossing a signal-free intersection with multiple lanes. A general framework is proposed to convert the multi-lane intersection problem into decentralized optimal control problems for each CAV. By combining optimal control and control barrier functions, the proposed method efficiently tracks feasible unconstrained CAV trajectories while ensuring the satisfaction of all safety constraints.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Chao Huang, Hailong Huang, Peng Hang, Hongbo Gao, Jingda Wu, Zhiyu Huang, Chen Lv
Summary: This paper develops a personalized approach for trajectory planning and control of autonomous vehicles based on user preferences, aiming to achieve safe, smart, and sustainable future mobility. By utilizing FLPR method and TOPSIS technique, the proposed method successfully satisfies users' various preferences and ensures vehicle safety under lane-change scenarios of autonomous driving.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Transportation Science & Technology
Rasool Mohebifard, Ali Hajbabaie
Summary: This paper introduces a methodology to optimize the trajectory of connected automated vehicles in roundabouts, reducing total travel times and average delays significantly compared to scenarios with human-driven vehicles.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Transportation
Jiali Peng, Shangguan Wei, Linguo Chai
Summary: This paper investigates the lane-changing behavior of heterogeneous traffic agents consisting of human-driven vehicles (HDVs) and connected-automated vehicles (CAVs). It proposes an optimization method for CAV lane changing under different penetration rates and examines the effectiveness of optimal control strategies through numerical simulations. The results show that these strategies improve the comfort, efficiency, safety, and stability of heterogeneous traffic agents compared to baseline maneuvers.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2023)
Article
Transportation Science & Technology
Zhen Wang, Xiaowei Shi, Xiangmo Zhao, Xiaopeng Li
Summary: This paper introduced a decentralized cooperative Mandatory Lane Change (MLC) framework for connected and autonomous vehicles (CAVs) to achieve safe MLC maneuvers with multiple-CAV cooperation. Numerical experiments showed that the proposed DC-MLC framework outperformed human drivers in terms of safety and performance metrics.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Engineering, Civil
Zhibo Gao, Zhizhou Wu, Wei Hao, Keke Long, Young-Ji Byon, Kejun Long
Summary: This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging, which achieves improvements in traffic safety and operational efficiencies by considering the lane-changing motivation of merging vehicles and impact of merging on platoons.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wen Hu, Zejian Deng, Yang Wu, Bangji Zhang, Wenbo Li, Xiaofeng Xiao, Bai Li, Dongpu Cao
Summary: This paper proposes a vehicle aggressiveness model based on asymmetric interactions between different types of vehicles. It provides a new perspective on asymmetric driving safety evaluation and heterogeneous driving behavior model in complex and mixed traffic environments.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Energy & Fuels
Gbalimene Richard Ileberi, Pu Li
Summary: Renewable energy resources and energy efficiency measures are effective ways to reduce CO2 emissions. This study investigates the potential of designing a hybrid system that incorporates hydrokinetic energy into an off-grid area. By applying a genetic algorithm to a small community in Nigeria, an optimal micro-grid configuration with PV panels, batteries, hydrokinetic turbines, converter, and a diesel generator is determined.
Article
Automation & Control Systems
Xinwei Wang, Bai Li, Xichao Su, Haijun Peng, Lei Wang, Chen Lu, Chao Wang
Summary: This paper proposes a search-resampling-optimization (SRO) framework for autonomous dispatch trajectory planning. It addresses the issue of low computational efficiency and numerical divergence in optimal control methods by using a hybrid A* algorithm for path generation and a resampling process for safe dispatch corridor creation.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Bai Li, Dongpu Cao, Shiqi Tang, Tantan Zhang, Hairong Dong, Yaonan Wang, Fei-Yue Wang
Summary: Replacing traffic signals with vehicle-to-infrastructure systems in the era of connected and autonomous vehicles (CAVs) offers promise. Autonomous intersection management (AIM), a signal-free intersection control system, improves traffic flow but lacks fairness consideration. This study proposes a near-optimal lane-free AIM method based on numerical optimal control and a parameterized social force model. The method enhances throughput while respecting individual fairness. Experiments demonstrate the efficiency and efficacy of the AIM method and the priority-sharing system.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Lili Fan, Dongpu Cao, Changxian Zeng, Bai Li, Yunjie Li, Fei-Yue Wang
Summary: In this research, a metaverse-enabled road maintenance system based on cyber-physical-social systems (CPSSs) is proposed, which fully utilizes the information of CPSS and the functions of road systems in the virtual world of the metaverse. An adaptive and information-preserving data augmentation (AIDA) algorithm-based crack detection algorithm is also proposed to enhance the detection performance of small targets in uncertain environments. The research can also be applied to the traffic metaverse by embedding a traffic flow prediction module in the algorithm. Experimental results show that the proposed algorithm outperforms other state-of-the-art algorithms in road damage detection tasks under different noise and weather conditions.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Bai Li, Ting Gao, Siji Ma, Youmin Zhang, Tankut Acarman, Kai Cao, Tian'ao Xu, Tantan Zhang, Fei-Yue Wang
Summary: This letter is the first report from a series of workshops on Intelligent Vehicles for Education, discussing the role of intelligent vehicles in promoting education through autonomous racing. It reviews the emergence of autonomous racing due to advancements in self-driving technologies and emphasizes the new chance for increased competitiveness and entertainment value without human drivers. The letter also introduces the new race series A1, leveraging autonomous intelligence in education, and discusses its future perspectives and the need to ensure race consistency, update rules, and provide personalized commentary for all-age education.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Haiping Du, Siyu Teng, Hong Chen, Jiaqi Ma, Xiao Wang, Chao Gou, Bai Li, Siji Ma, Qinghai Miao, Xiaoxiang Na, Peijun Ye, Hui Zhang, Guiyang Luo, Fei-Yue Wang
Summary: This letter presents a decentralized and hybrid workshop on the potential influence of ChatGPT on research and development in intelligent vehicles. The tests conducted showed that while ChatGPT's information can be updated and corrected, it may not always possess the latest knowledge regarding specific topics. The letter also discusses possible applications of ChatGPT in areas like autonomous driving, human-vehicle interaction, and intelligent transportation systems, highlighting challenges and opportunities associated with these applications.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Long Chen, Yuchen Li, Chao Huang, Bai Li, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Xiaoxiang Na, Zixuan Li, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Summary: Interest in autonomous driving and intelligent vehicles is growing rapidly due to their convenience, safety, and economic benefits. However, existing surveys are limited in scope and lack systematic summaries and future research directions.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Bai Li, Yakun Ouyang, Xiaohui Li, Dongpu Cao, Tantan Zhang, Yaonan Wang
Summary: Trajectory planning for a heavy-duty mining truck near the loading/dumping sites of an open-pit mine is challenging due to complex factors in vehicle kinematics and environment. This necessitates a mixed-integer nonlinear program (MINLP) with conditional constraints. To overcome the limitations of MINLP solvers, a coarse-to-fine framework is proposed to divide and conquer the coupled difficulties, resulting in an efficient conversion from the C-MINLP to a small-scale NLP.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Automation & Control Systems
Long Chen, Siyu Teng, Bai Li, Xiaoxiang Na, Yuchen Li, Zixuan Li, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
Summary: The growing interest in autonomous driving and intelligent vehicles is driven by their promise of improved safety, efficiency, and economic benefits. This work fills a gap in the field by providing a comprehensive overview of the history, ethics, and future directions of AD and IV technologies. It delves into the development of control systems, computing systems, communication, HD mapping, testing, and human behaviors in IVs, while also reviewing perception and planning in the context of IVs.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Lili Fan, Shen Li, Ying Li, Bai Li, Dongpu Cao, Fei-Yue Wang
Summary: Automatic pavement crack detection is crucial for maintaining road stability and driving safety. However, the interference caused by shadows hinders the performance of crack detection. To address this issue, we propose a new dataset, a two-step shadow-removal-oriented crack detection approach, and a data augmentation method to cope with brightness changes. We also introduce a residual feature augmentation algorithm to detect small cracks and improve the overall performance of the model.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2023)
Article
Computer Science, Artificial Intelligence
Yonglin Tian, Xuan Li, Hui Zhang, Chen Zhao, Bai Li, Xiao Wang, Xiao Wang, Fei-Yue Wang
Summary: This article proposes a Transformer-based unified framework, VistaGPT, to address the challenges posed by heterogeneous vehicle automation systems. It overcomes information barriers through modular federations and automated composition, utilizing large language models for generating autonomous driving systems. Scenario engineering systems are deployed for evaluation and optimization.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Bai Li, Yile Fang, Tian'ao Xu, Siji Ma, Haorui Wang, Yazhou Wang, Xinyuan Li, Tantan Zhang, Xuepeng Bian, Fei-Yue Wang
Summary: This letter is the second report from a series of workshops on Intelligent Vehicles for Education, outlining the prospect of a future autonomous racing competition called The Autonomous One (TAO). The focus lies in setting governance rules to ensure fairness, increase spectator interest, inspire competitiveness, and contribute to the advancement of intelligent vehicle education.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yuchen Li, Yunfeng Ai, Dongsheng Yang, Lingxi Li, Zhe Xuanyuan, Fenghua Zhu, Long Chen
Summary: Intelligent vehicles (IVs) have received global attention for their increased convenience, safety advantages, and potential commercial value. Despite the prediction of commercial deployment by 2025, the implementation of IVs remains limited to small-scale validation, requiring precise tracking controllers and motion planners. This article reviews the state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. It discusses the selection, expansion, and optimization operations in pipeline methods, as well as the training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Bai Li, Yazhou Wang, Siji Ma, Xuepeng Bian, Hu Li, Tantan Zhang, Xiaohui Li, Youmin Zhang
Summary: This article introduces an Adaptive Pure Pursuit (APP) planner, which performs fast and near-optimal path planning for autonomous driving in cluttered environments by generating feasible paths through a simulated closed-loop tracking control process of a virtual vehicle.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)