Article
Engineering, Civil
Donghao Xu, Zhezhang Ding, Xu He, Huijing Zhao, Mathieu Moze, Francois Aioun, Franck Guillemard
Summary: The study proposes a method of learning cost parameters of a motion planner from naturalistic driving data to achieve human-like driving behavior in autonomous vehicles. The motion planner incorporates incentive of behavior decision like a human driver, and promising results are achieved in experiments conducted with respect to both lane change decision and motion planning.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiao Wang, Ke Tang, Xingyuan Dai, Jintao Xu, Jinhao Xi, Rui Ai, Yuxiao Wang, Weihao Gu, Changyin Sun
Summary: This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs) in complex and uncertain intersection scenarios, considering interactions with other human-driving vehicles (HDVs) with different driving styles. The method utilizes a mixture-of-experts approach to learn from human-driving trajectory data and construct a multimodal motion planner, which explicitly models the interactions between vehicles and enables scene-consistent multimodal trajectory prediction and candidate trajectory generation. The generated trajectories are evaluated using a safety-balanced value function, and the trajectory with the highest value is selected for implementation. Experimental results demonstrate the efficiency, effectiveness, robustness, and reasonableness of the method in intersection scenarios with HDVs' behavioral dynamics.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Information Systems
Xianjian Jin, Zeyuan Yan, Guodong Yin, Shaohua Li, Chongfeng Wei
Summary: This paper presents a hierarchical motion planning approach based on discrete optimization method for on-road autonomous driving. It uses well-coupled longitudinal and lateral planning strategies to achieve better performance, with re-determination of longitudinal horizon and update of speed profile for re-planning if candidate paths ahead fail the safety checking. A pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of the autonomous vehicle.
Article
Robotics
Mingyu Wang, Zijian Wang, John Talbot, J. Christian Gerdes, Mac Schwager
Summary: This article presents a nonlinear receding horizon game-theoretic planner specifically designed for autonomous cars in competitive scenarios, providing rich game strategies for trajectories through constraints and optimizations on trajectory and bicycle kinematics. The planner iteratively plans trajectories for the ego vehicle and other vehicles, incorporating a sensitivity term for collision avoidance and significantly outperforming a baseline planner in numerical simulations and experiments.
IEEE TRANSACTIONS ON ROBOTICS
(2021)
Article
Robotics
Jiawei Fu, Xiaotong Zhang, Zhiqiang Jian, Shitao Chen, Jingmin Xin, Nanning Zheng
Summary: Velocity planning is crucial for autonomous driving as it generates the velocity profile based on a reference path. However, existing algorithms often neglect uncertainties in driving contexts, leading to potential risks. To address this, we propose an efficient safety-enhanced velocity planning algorithm (ESEVP) that considers uncertainties from trajectory prediction and velocity tracking using chance constraints. ESEVP formulates velocity planning as quadratic programming and explores candidate solutions through a fast planning space construction method, ensuring efficiency and covering all interaction possibilities. Experimental results prove ESEVP's superiority in terms of safety, comfort, and driving efficiency, and its successful deployment in real traffic demonstrates its practical competitiveness.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Ergonomics
Yin Zheng, Xiang Wen, Pengfei Cui, Huanqiang Cao, Hua Chai, Runbo Hu, Rongjie Yu
Summary: Driving behavior intervention is an effective measure to reduce crash occurrence, but it faces challenges in terms of multiple intervention locations and measures. This study proposes a counterfactual safety benefits quantification method and shows that safety broadcasting can significantly reduce driving speed and speeding-related crashes.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Engineering, Civil
Christoph Ziegler, Volker Willert, Jurgen Adamy
Summary: This paper focuses on modeling human driving behavior and its application in trajectory planning. By using a time-discrete kinematic bicycle model, the study investigates the modeling of human driven trajectories under different sampling times, and finds that longer sampling times can result in smoother and more efficient trajectories. Additionally, the correlations between the model inputs and the current state/last input are analyzed, and nonlinear transformations are proposed to simplify planning algorithms.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Wonteak Lim, Seongjin Lee, Myoungho Sunwoo, Kichun Jo
Summary: This paper proposes a hybrid trajectory planning scheme that integrates the strength of sampling and optimization methods to solve the problem of safe trajectory planning in dynamic driving environments. The sampling method is used for lateral movement while the numerical optimization method is used for longitudinal movement, helping the planner generate adaptive trajectories in various situations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Robbin Van Hoek, Jeroen Ploeg, Henk Nijmeijer
Summary: In this research, a method is proposed to combine the flexibility and cooperation of autonomous vehicles by using a trajectory planner to generate cooperative trajectories. B-splines are used to ensure string stable behavior even under large communication delays. The use of parametrized trajectories allows for only a small number of parameters to be communicated between vehicles.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2021)
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)
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
Engineering, Civil
Chao Huang, Hailong Huang, Junzhi Zhang, Peng Hang, Zhongxu Hu, Chen Lv
Summary: This paper proposes a human-machine cooperative trajectory planning and tracking control approach for automated vehicles, which assesses driver behavior risks and utilizes the new HM-RRT algorithm for path planning to ensure the safety, stability, and smoothness of the human-vehicle system.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Omveer Sharma, N. C. Sahoo, N. B. Puhan
Summary: Autonomous vehicles are gaining attention in academic and industrial research due to their advantages such as safety improvement and reduced traffic congestion. Intelligent motion and behavior planning play crucial roles in decision making process, considering factors like safety, comfort, and traffic rules. Various techniques have been developed over the past few decades, but there is still a need for rigorous evaluation and improvement of existing approaches.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Engineering, Electrical & Electronic
Han Li, Guizhen Yu, Bin Zhou, Peng Chen, Yaping Liao, Da Li
Summary: The study proposes a semantic-level maneuver sampling and trajectory planning algorithm, which samples long-term maneuver sequences in upper-level decision-making, separates the process into longitudinal and lateral directions in lower-level trajectory planning, and combines heuristic search and exhaustive search methods to choose the trajectory with minimum cost as the result. Numerical optimization is also used to refine the driving comfort, demonstrating desirable computation efficiency of less than 32 ms in simulations of typical on-road dynamic scenarios.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Automation & Control Systems
Runqi Chai, Antonios Tsourdos, Senchun Chai, Yuanqing Xia, Al Savvaris, C. L. Philip Chen
Summary: This article studies the problem of trajectory optimization for autonomous ground vehicles with the consideration of irregularly placed on-road obstacles and multiple maneuver phases. It proposes a novel desensitized trajectory optimization method to provide an effective alternative for addressing the complexity of the mission formulation.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Civil
Yunpeng Wang, Junjie Zhang, Guangquan Lu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Engineering, Civil
Peng Chen, Rui Tong, Guangquan Lu, Yunpeng Wang
JOURNAL OF ADVANCED TRANSPORTATION
(2018)
Article
Computer Science, Information Systems
Yunpeng Wang, Xuting Duan, Daxin Tian, Jianshan Zhou, Yingrong Lu, Guangquan Lu
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2014)
Article
Engineering, Civil
Pinlong Cai, Yunpeng Wang, Guangquan Lu
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Ergonomics
Mengxia Jin, Guangquan Lu, Facheng Chen, Xi Shi, Haitian Tan, Junda Zhai
Summary: This study used a structural equation model to analyze the variation in drivers' takeover performance in Level 3 automated vehicles, revealing the complex relationships among takeover time budget, trust, driver characteristics, environmental characteristics, and vehicle characteristics. The findings emphasized the crucial role of trust in the change in takeover behavior and highlighted the importance of subjective trust level and monitoring strategy in the function design of takeover process.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Engineering, Civil
Rongjian Dai, Chuan Ding, Xinkai Wu, Bin Yu, Guangquan Lu
Summary: This study proposes a two-dimensional control strategy for isolated signalized intersections, which optimizes traffic signals, lane settings, and vehicle trajectories in a mixed traffic environment. The proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Guangquan Lu, Jun Hua, Haoyi Zhao, Miaomiao Liu, Jin Xu, Fang Zong
Summary: This study establishes a unified model to describe non-conflict paths of vehicles through intersections and validates the model using actual path data. The results demonstrate the high effectiveness of the proposed model, making it applicable to various types of intersections. The study provides an important opportunity for planning local paths for autonomous vehicles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Proceedings Paper
Engineering, Civil
Facheng Chen, Guangquan Lu, Junda Zhai, Haitian Tan
INTERNATIONAL CONFERENCE ON TRANSPORTATION AND DEVELOPMENT 2020: TRANSPORTATION SAFETY
(2020)
