Article
Engineering, Mechanical
Yanhong Wu, Zhiqiang Zuo, Yijing Wang, Qiaoni Han
Summary: This paper proposes a driver-centric data-driven robust model predictive control (DDRMPC) strategy to improve the driving safety and comfort of mixed vehicular platoons (MVP) consisting of human-driven vehicles (HDVs) and automated vehicles (AVs). The strategy involves a data-driven MVP model, personalized driving policy, and a tube-based robust model predictive controller. Experiments on a self-developed MVP platform demonstrate the effectiveness of the proposed DDRMPC strategy.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Civil
Shixi Wen, Ge Guo
Summary: This study introduces a distributed hierarchical framework for solving the problem of distributed trajectory optimization and platooning of heterogeneous vehicles. The framework utilizes convex optimization and adaptive sliding mode controller to achieve optimal trajectory tracking for the vehicles while compensating for uncertain vehicle dynamics with a parameter adaptation law. The controller parameters are determined to ensure both internal and string stability in the context of sliding mode control based on tracking error dynamics.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yuan Zhao, Zhongchang Liu, Wing Shing Wong
Summary: This paper investigates the platoon control problem for Vehicular Cyber Physical Systems (VCPSs) under Denial-of-Service (DoS) attacks and multiple disturbances. A recovery mechanism is introduced to confine the effects of DoS attacks on VCPSs, and a resilient platoon control protocol is proposed to achieve internal stability. A controller design algorithm is used to minimize disturbance propagation bound under DoS attacks. Numerical examples demonstrate the effectiveness of the proposed theoretical results.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yongming Li, Yongyan Zhao, Shaocheng Tong
Summary: This article investigates the problem of fuzzy adaptive asymptotic tracking control for a third-order heterogeneous vehicular platoon system with input saturation. The unknown nonlinear functions are approximated using fuzzy logic systems, and a control scheme with an auxiliary design system is proposed to address the issue of input saturation. A spacing error is created to reduce the intervehicle spacing, and the proposed scheme employs the barrier Lyapunov functions to impose distance restrictions, ensuring collision avoidance and maintaining communication connections.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Liwei Xu, Xianjian Jin, Yan Wang, Ying Liu, Weichao Zhuang, Guodong Yin
Summary: This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. The protocol considers the random variation of data transmission among the platoon, as well as factors like delay in wireless communication and external interferences. The proposed control approach ensures the stability of the platoon even in the continuous mutation of unstable topologies.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Engineering, Civil
Chengwei Pan, Yong Chen, Yuezhi Liu, Ikram Ali
Summary: This paper investigates the problem of handling actuator fault, actuator saturation, and complex environmental disturbances in vehicle platooning. An adaptive control scheme is proposed to improve system stability, and the finite-time string stability of the system is proven.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Fangfang Dong, Xiaomin Zhao, Ye-Hwa Chen
Summary: This article discusses the control design problem of a fuzzy vehicular platoon system, employing fuzzy theory to describe the system and proposing a switching-type adaptive robust control. Control parameter optimization is further explored to achieve system stability and collision avoidance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Civil
Xiang-Gui Guo, Wei-Dong Xu, Jian-Liang Wang, Ju H. Park, Huaicheng Yan
Summary: This paper investigates the neuroadaptive fault-tolerant control of a nonlinear vehicular platoon with unmodeled dynamics, external disturbances, time-varying actuator fault directions, and distance restrictions. Two neuroadaptive fault-tolerant controllers are designed based on adaptive terminal sliding mode control technique and barrier Lyapunov function to ensure reliability and safety. The proposed scheme effectively solves the influence of unknown time-varying fault directions and ensures spacing error convergence through nonsingular TSM control technique and RBFNN method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Information Systems
Chengwei Pan, Yong Chen, Songge Chen, Ikram Ali
Summary: This article focuses on the problem of distributed adaptive fixed-time fuzzy resilient control (DAFTFRC) for the heterogeneous connected vehicular platoon (HCVP) system subject to deception attacks and unknown external disturbances. An effective fixed-time disturbance observer (FTDO) is introduced to estimate the unknown external disturbance of the platoon. The DAFTFRC framework is designed to ensure internal stability and string stability under deception attacks by using the improved event-triggered mechanism (IETM).
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Yanhong Wu, Zhiqiang Zuo, Yijing Wang, Qiaoni Han, Chuan Hu
Summary: In order to alleviate the adverse effects of heterogeneous vehicular platoon (HVP) with uncertain dynamics, this paper proposes a distributed data-driven model predictive control (DDMPC) strategy. A data-driven model is established using subspace identification based on the input-output (I/O) vehicle trajectory. The DDMPC strategy integrates the data-driven model with the distributed model predictive control (MPC) algorithm to optimize the control of the HVP.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Zeyu Yang, Jin Huang, Diange Yang, Zhihua Zhong
Summary: This paper presents a hierarchical control framework for a connected and automated vehicular platoon to minimize fuel consumption and ensure collision-free property. The upper layer involves a centralized ecological speed planning algorithm based on dynamic programming, while the lower layer utilizes a distributed collision-free speed tracking control method to guarantee collision-free property.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Rene Oliveira, Carlos Montez, Azzedine Boukerche, Michelle S. Wangham
Summary: This paper presents a consensus-based solution called AddP-CACC for vehicle platoon driving, which dynamically reconfigures the controller and automatically compensates for outdated information caused by network losses and delays. Simulation results demonstrate the robustness and performance of the proposed solution in various scenarios with interference and fading conditions.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Engineering, Civil
Chengwei Pan, Yong Chen, Yuezhi Liu, Ikram Ali, Wen He
Summary: This paper investigates the distributed fault-tolerant control problem for the heterogeneous vehicular platoon system suffering from actuator faults, saturation, and external disturbances. An exponential spacing policy and a nonlinear observer are developed to tackle the stability issues. A distributed adaptive fault-tolerant control scheme based on bidirectional-leader information flow topology is proposed to achieve the desired stability and fault tolerance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Hao Zhang, Juan Liu, Zhuping Wang, Huaicheng Yan, Changzhu Zhang
Summary: This paper proposes a novel control framework for the vehicular platoon, including a distributed adaptive event-triggered observer and a car-following control protocol, to ensure all vehicles travel at the same speed and maintain safety spacing. By designing a distributed event-triggered observer that considers collision avoidance and limited communication source for each vehicle, the stability of the platoon is achieved.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Handong Li, Haimeng Wu, Ishita Gulati, Saleh A. Ali, Volker Pickert, Satnam Dlay
Summary: This paper presents a novel controller that combines fuzzy logic with PID control to address uncertainties and communication delays in vehicle platooning, resulting in improved driving safety and reduced traffic congestion.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jiaxin Gao, Yang Guan, Wenyu Li, Shengbo Eben Li, Fei Ma, Jianfeng Zheng, Junqing Wei, Bo Zhang, Keqiang Li
Summary: This research introduces a time-independent algorithm T3S, which improves the training efficiency of the MBPG algorithm for solving the optimal control problem by using the time-splitting technique, and further accelerates the training process with an asynchronous parallel training scheme.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Editorial Material
Engineering, Electrical & Electronic
Guofa Li, Cristina Olaverri-Monreal, Xiaobo Qu, Changxu Sean Wu, Shengbo Eben Li, Hamid Taghavifar, Yang Xing, Shen Li
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Automation & Control Systems
Fuguo Xu, Hiroki Tsunogawa, Junichi Kako, Xiaosong Hu, Shengbo Eben Li, Tielong Shen, Lars Eriksson, Carlos Guardiola
Summary: This paper proposes a benchmark problem for the energy efficiency control of hybrid electric vehicles (HEVs) on roads with slopes in a connected environment. By providing a simulator and a case study, it demonstrates the method of powertrain control of HEVs using traffic information.
CONTROL THEORY AND TECHNOLOGY
(2022)
Article
Engineering, Civil
Ziyu Lin, Jun Ma, Jingliang Duan, Shengbo Eben Li, Haitong Ma, Bo Cheng, Tong Heng Lee
Summary: In the field of autonomous driving, it is challenging to solve the motion planning problem due to the nonlinearity of the vehicle model and the complexity of driving scenarios. To address this, a framework of integrated decision and control is investigated, using static path planning and an innovative constrained finite-horizon approximate dynamic programming algorithm. This algorithm effectively handles changing driving environments with varying surrounding vehicles and reduces computational loads through offline training and online execution.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Yang Guan, Yangang Ren, Qi Sun, Shengbo Eben Li, Haitong Ma, Jingliang Duan, Yifan Dai, Bo Cheng
Summary: This article presents an interpretable and computationally efficient framework called integrated decision and control (IDC) for automated vehicles. It decomposes the driving task into static path planning and dynamic optimal tracking that are structured hierarchically. The framework has been verified in both simulations and the real world, showing improved online computing efficiency, driving performance, as well as interpretability and adaptability in different driving scenarios and tasks.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Robotics
Yujie Yang, Yuxuan Jiang, Yichen Liu, Jianyu Chen, Shengbo Eben Li
Summary: This letter proposes a model-free safe reinforcement learning algorithm that achieves near-zero constraint violations with high rewards. By jointly learning a policy and a neural barrier certificate under stepwise state constraint setting, our algorithm balances the bias and variance of the barrier certificate and enhances both safety and performance.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Yongxin Zhu, Yongfu Li, Hao Zhu, Wei Hua, Gang Huang, Shuyou Yu, Shengbo Eben Li, Xinbo Gao
Summary: This article presents a jointly distributed adaptive sliding-mode controller (DASMC) and disturbance observer (DO) design for a heterogeneous platoon of connected vehicles (CVs) subject to disturbance and acceleration failure of neighboring vehicles under different common communication topologies. The effects of disturbance and acceleration failure are mitigated by jointly estimating the lumped disturbance arising from vehicle heterogeneity, nonlinearity, and neighboring vehicle acceleration using a DO. The DASMC, with an adaptive law based on defined lumped errors, is designed according to the vehicle dynamic model, and its stability is analyzed using the Lyapunov technique. The string stability of the CV platoon is also proven. The effectiveness of the proposed control scheme is demonstrated through simulations and experiments, comparing it with existing methods.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Jingliang Duan, Jie Li, Qiang Ge, Shengbo Eben Li, Monimoy Bujarbaruah, Fei Ma, Dezhao Zhang
Summary: This paper introduces the Relaxed Continuous-Time Actor-critic (RCTAC) algorithm, which aims to find the nearly optimal policy for nonlinear continuous-time systems. RCTAC has advantages over existing adaptive dynamic programming algorithms as it does not require specific conditions for convergence. The algorithm consists of a warm-up phase and a generalized policy iteration phase, where admissibility and convergence are achieved through minimizing the square of the Hamiltonian and relaxing the update termination conditions.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Feng Gao, Yu Han, Shengbo Eben Li, Shaobing Xu, Dongfang Dang
Summary: This paper presents an NMPC based motion planner for automated vehicles and introduces two techniques, adaptive Lagrange discretization and hybrid obstacle avoidance constraints, to accelerate numerical optimization. The techniques reduce optimization variables and simplify non-convex constraints. The Lagrange interpolation is adopted to ensure accuracy with fewer discretization points, and an adaptive strategy adjusts the order of Lagrange polynomials based on the discretization error. A hybrid strategy combines elliptic and linear time-varying methods to construct obstacle avoidance constraints. Comparative simulations and tests show that these techniques improve accuracy and efficiency by 74% and 60%, respectively.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Guofa Li, Yifan Qiu, Yifan Yang, Zhenning Li, Shen Li, Wenbo Chu, Paul Green, Shengbo Eben Li
Summary: End-to-end approaches are a promising solution for AV decision-making, but their deployment is often hindered by high computational burden. To address this, we propose a lightweight transformer-based end-to-end model with risk awareness for AV decision-making. We introduce a lightweight network with depth-wise separable convolution and transformer modules to extract image semantics from trajectory data. We then assess driving risk using a probabilistic model with position uncertainty and integrate it into deep reinforcement learning to find strategies with minimum expected risk. The proposed method is evaluated in three lane change scenarios to validate its superiority.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Lian Hou, Shengbo Eben Li, Bo Yang, Zheng Wang, Kimihiko Nakano
Summary: This paper presents a unified graphical representation method that takes into account the varying numbers and types of vehicles, various road structures, and traffic rules to improve trajectory prediction for autonomous vehicles in highway traffic scenarios. By integrating the constraints from vehicles and the collision risk implied behind road structures and traffic rules, this method provides a quantitative way to better utilize the influences of these elements on trajectory prediction.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Civil
Ziqing Gu, Lingping Gao, Haitong Ma, Shengbo Eben Li, Sifa Zheng, Wei Jing, Junbo Chen
Summary: This paper proposes a state-based safety enhancement method for autonomous driving through direct hierarchical reinforcement learning. By integrating a dynamic module and generating future goals considering safety, temporal-spatial continuity, and dynamic feasibility, the proposed method shows better training performance, higher driving safety in interactive scenes, more decision intelligence in traffic congestions, and better economic driving ability on roads with changing slopes.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ziyu Lin, Jingliang Duan, Shengbo Eben Li, Haitong Ma, Jie Li, Jianyu Chen, Bo Cheng, Jun Ma
Summary: The research addresses the challenge of solving the finite-horizon HJB equation, proposes a new algorithm, and validates its effectiveness through simulations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Haotian Zheng, Chaoyi Chen, Shuai Li, Sifa Zheng, Shengbo Eben Li, Qing Xu, Jianqiang Wang
Summary: By using magnitude regularization techniques, this study proposes a method to enhance the efficiency of safe controllers by reducing conservativeness within the energy function while maintaining promising demonstrable safety guarantees.
IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Automation & Control Systems
Jingliang Duan, Jie Li, Xuyang Chen, Kai Zhao, Shengbo Eben Li, Lin Zhao
Summary: This article investigates the optimization landscape of policy gradient methods for static output feedback control in discrete-time LTI systems. By analyzing the crucial properties of the SOF cost, new findings on convergence and convergence rate for three policy gradient methods are derived, along with a proof of linear convergence for the vanilla policy gradient method when initialized near local minima. Numerical examples validate the theoretical findings. These results characterize the performance of gradient descent for optimizing the SOF problem and provide insights into the effectiveness of general policy gradient methods within the realm of reinforcement learning.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
