Article
Engineering, Electrical & Electronic
Zhida Zhang, Ling Zheng, Yinong Li, Shaohua Li, Yixiao Liang
Summary: This paper proposes a novel cooperative strategy for trajectory tracking and stability control of four-wheel independent drive (4WID) autonomous vehicles. An adaptive trajectory tracking controller is designed using the model predictive control (MPC) theory, which modifies the tire cornering stiffness in real-time based on the estimated lateral force. A vehicle stability controller is then designed based on sliding mode control (SMC), considering tire force saturation constraint and relative weight of yaw rate and sideslip angle. The feasibility and adaptability of the proposed method are verified through hardware-in-the-loop (HIL) test and CarSim-Simulink co-simulation. An experimental case is presented to demonstrate the realizability of the proposed method.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Civil
Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
Summary: Autonomous driving systems have seen significant development due to advances in machine learning algorithms. However, safety evaluation remains a critical challenge for their widespread deployment. Most existing systems are trained and evaluated on real-world scenarios, but safety-critical scenarios are rare in collected data. Therefore, methods to artificially generate scenarios become crucial for measuring risk and reducing costs.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Peng Hang, Xin Xia, Guang Chen, Xinbo Chen
Summary: This research focuses on the collaborative control of four-wheel steering and direct yaw-moment control to enhance the active safety performance of automated electric vehicles. The study applies tube-based model predictive control to deal with disturbance and modeling error, and successfully designs an integrated controller that improves handling stability and path-tracking performance. Experimental tests prove the effectiveness and robustness of the integrated controller under extreme driving conditions.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Chemistry, Multidisciplinary
Bohan Jiang, Xiaohui Li, Yujun Zeng, Daxue Liu
Summary: The paper introduces a novel safe driving envelope generation method to evaluate the risk of candidate driving maneuvers by considering various constraints. The proposed method's efficiency is validated through simulation experiments and real vehicle tests, demonstrating its feasibility for real-time applications.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Civil
Angelo Coppola, Dario Giuseppe Lui, Alberto Petrillo, Stefania Santini
Summary: This work proposes a novel Eco-Driving Control Architecture to address the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon. The architecture consists of a Nonlinear Model Predictive Control strategy and a distributed exponentially-stable robust PID-like protocol to achieve precise leader-tracking and energy-saving control.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Chen Tang, Amir Khajepour
Summary: This study proposes an integrated stability control strategy for tilting vehicles, extending the envelope-based lateral stability controller and utilizing a model predictive controller to address the non-minimum phase problem. The simulation results show that the control effort for maintaining the roll stability of narrow vehicles can be greatly reduced with the envelope-based control scheme. The integrated controller also improves vehicle handling performance while ensuring lateral and roll stability.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Ge Guo, Yunpeng Wang
Summary: This paper investigates a cooperative optimization framework for eco-driving and signal priority for freight vehicles on signalized arterial roads. By optimizing speed trajectories and signal timing, the goal is to minimize fuel consumption of freight vehicles and queueing delay of passenger vehicles at intersections, with the aim of achieving freight-priority. A sub-optimal strategy is proposed to address the non-convex optimization problem caused by constraints from queues and traffic signals. Comparison studies show significant improvement in traffic efficiency and fuel economy with faster calculation.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Xianbin Wang, Zexuan Li, Fugang Zhang, Weifeng Li, Wenlong Bao
Summary: This paper proposes a vehicle control strategy evaluation method based on the driving stability region and analyzes the effectiveness of direct yaw moment control (DYC) and four-wheel steering (4WS) using established 5DOF vehicle system models. A hybrid algorithm combining genetic algorithm (GA) and sequential quadratic programming (SQP) is used to solve system equilibrium points and obtain the driving stability regions under different control strategies. The results indicate that DYC and 4WS can improve vehicle stability and expand the range of driving stability regions, with DYC showing better control effect than 4WS.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Xiao Hu, Ping Wang, Shuo Cai, Lin Zhang, Yunfeng Hu, Hong Chen
Summary: Good stability performance is crucial for vehicles controlled by human drivers or highly automated driving systems. However, vehicles may lose motion stability even with active motion controllers. In this work, we analyze the zero dynamics of controller design and identify the conditions under which vehicles start to skid or spin. Based on the analysis, we propose a cooperative control scheme to improve motion stability, which is validated through simulations and experiments with a refitted DongFeng E70 vehicle.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Liuwang Kang, Haiying Shen, Yezhuo Li, Shiwei Xu
Summary: This article proposes a data-driven control policy-based driving safety analysis system (PoSa) to analyze the driving safety of a target autonomous vehicle (AV). By extracting the control policies and identifying possible hazardous driving scenarios, this system can optimize control policies for driving safety improvement.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Chemistry, Analytical
Liping Wu, Ran Zhou, Junshan Bao, Guang Yang, Feng Sun, Fangchao Xu, Junjie Jin, Qi Zhang, Weikang Jiang, Xiaoyou Zhang
Summary: The study focused on improving vehicle stability under extreme operating conditions using linear quadratic regulator control and electromagnetic active suspension. By establishing a 7-DOF dynamics model and designing an LQR controller, significant improvements in stability were achieved during high-speed driving and low adhesion turning. Simulation results validated by real vehicle tests showed promising results for reducing driving risk under extreme working conditions.
Article
Automation & Control Systems
Nathan A. Spielberg, Matthew Brown, J. Christian Gerdes
Summary: NNMPC is a method that constructs a neural network model using vehicle operation data to predict vehicle dynamics and perform model predictive control in complex operating conditions. Experimental results demonstrate the capability of NNMPC to follow trajectory near the limits on high- and low-friction test courses, outperforming physics-based MPC when environmental latent state is considered.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2022)
Article
Engineering, Civil
Lin Zhang, Bin Li, Yi Hao, Haoqi Hu, Yunfeng Hu, Yanjun Huang, Hong Chen
Summary: This paper proposes a new simultaneous planning and control scheme to address the problem of conservative or impossible trajectory tracking on slippery roads caused by existing hierarchical planning and control architectures. The scheme establishes vehicle stability boundary and safety distance constraints, and utilizes real-time adaptive model predictive control to approximate nonlinear programming as quadratic program for fast optimization. The proposed algorithm significantly improves stability and driving comfort, as demonstrated by simulation results.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Mohamed A. Daoud, Mohamed W. Mehrez, Derek Rayside, William W. Melek
Summary: This paper proposes a new approach for lane change and double-lane change planning and following for autonomous driving. The approach utilizes exponential functions to generate online and feasible lane change maneuvers, and adopts a simultaneous local path planning and path-following control framework. Real-time simulation scenarios are used to validate the effectiveness of the proposed approach in generating and smoothly following lane change maneuvers.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Zihan Li, Ping Wang, Shuo Cai, Xiao Hu, Hong Chen
Summary: This paper proposes a real-time NMPC-based controller to improve the longitudinal and lateral stability of four-wheel independent motor-drive electric vehicles under extreme driving conditions. The stability controller is developed using a combined slip tire model on low friction coefficient surfaces. The controller achieves slip control, lateral stability control, and handling performance improvement by selecting wheel slip ratios and slip angles as virtual control inputs. The control performance is investigated through co-simulation and the robustness of the controller is verified with uncertainties.
Article
Engineering, Electrical & Electronic
Qingyu Meng, Hongyan Guo, Yanran Liu, Hong Chen, Dongpu Cao
Summary: This paper proposes a neighboring vehicle trajectory prediction method based on graph neural networks (GNNs), which extracts lane line positions and attribute information from high-definition (HD) maps and introduces an ice and snow mask mechanism to simulate lane line occlusion. Experimental results show that accurate vehicle trajectory prediction can be achieved on roads covered with ice and snow.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Lin Chen, Yilin Wang, Jing Zhao, Shihong Ding, Jinwu Gao, Hong Chen
Summary: An adaptive control scheme is proposed based on extremum seeking (ES) for rapid and high-precision servo control of an electronic throttle. It consists of an ES-variable-gain adaptive proportional-integral (ES-API) controller and an adaptive compensator (ES-ACP). The ES-API controller uses maps for the gains designed with respect to the tracking error and the ES-ACP compensates for the nonlinearity inherent in the electronic throttle control (ETC) system. Experimental results demonstrate that the control scheme is capable of accurately and quickly tracking multiple reference trajectories.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2023)
Article
Engineering, Civil
Lin Zhang, Lianbo Jiang, Hanghang Liu, Yunfeng Hu, Ping Wang, Hong Chen
Summary: This study proposes a hierarchical control strategy to improve vehicle stability under extreme conditions. In the upper layer controller, a combined-slip tire model is adopted to improve model accuracy. A nonlinear model predictive control based controller is designed to track desired yaw rate and suppress lateral velocity and tire slip ratios. In the lower layer controller, the disturbance on the driver's torque requirement is taken into account and a linear predictive controller is designed to adjust motor torques to track desired tire slip ratios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Mechanical
Hongyan Guo, Xu Zhao, Jun Liu, Qikun Dai, Hui Liu, Hong Chen
Summary: An estimation framework that combines vision and vehicle dynamic information is established to accurately obtain the peak tire-road friction coefficient. The framework collects information for the road ahead from an image captured by a camera and uses a lightweight convolutional neural network to identify the road type and its corresponding range of tire-road friction coefficients. An unscented Kalman filter (UKF) method is then used to estimate the tire-road friction coefficient value directly based on the dynamic vehicle states. The results from the road-type recognition and dynamic estimation methods are synchronized, and a confidence-based fusion strategy is proposed to obtain an accurate peak tire-road friction coefficient. Virtual and real vehicle tests confirm the effectiveness of the proposed fusion estimation strategy, which outperforms both general vision-based estimation methods and dynamic-based estimation methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(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
Yao Sun, Yunfeng Hu, Hui Zhang, Hong Chen, Fei-Yue Wang
Summary: This letter presents the latest research findings from IEEE TIV's Decentralized and Hybrid Workshops (DHW) on Ethics, Responsibility, and Sustainability (ERS). The research focuses on a novel emission regulatory framework for intelligent transportation systems (ITS) and smart cities (SCs) in ERS. The framework proposes a parallel transportation level and a parallel vehicle level to achieve accurate estimation and emission-aware optimal planning. The letter also highlights the importance of considering modern aftertreatment systems (ATS) as a core module in the emission model.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Yao Sun, Yunfeng Hu, Hui Zhang, Feiyue Wang, Hong Chen
Summary: A parallel supervision system is developed to accurately estimate vehicle CO2 emissions by using only onboard diagnostics (OBD)-independent information. The system can predict future road gradients and planned speed trajectories. The combined CO2 model, consisting of physical and data-driven models, is considered the core part of the artificial world, while the actual traffic environment is regarded as the physical world. Two real-world experimental case studies validate the accuracy of both the physical and data-driven models, with the physical model showing more robustness. The system effectively bridges the gap between regulatory test cycles and real-world carbon emissions.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Computer Science, Artificial Intelligence
Lulu Guo, Hongqing Chu, Jin Ye, Bingzhao Gao, Hong Chen
Summary: This paper proposes a hierarchical velocity control system considering different drive preferences for connected and automated vehicles to improve overall efficiency under the vehicle-to-X (V2X) environment. Simulation results indicate that energy-saving and computational efficiency are improved using the proposed control system. The solution algorithm is further demonstrated under a hardware-in-the-loop simulation.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Civil
Xinghao Lu, Haiyan Zhao, Cheng Li, Bingzhao Gao, Hong Chen
Summary: In this paper, a new decision-making method based on game theory is proposed to resolve the driving conflict and improve the safety and efficiency of autonomous vehicles at unsignalized intersections. The proposed method constructs an alterable game mode by combining a designed game entrying mechanism and replanning of game sequential order under different intersection conditions to ensure adaptiveness and effectiveness. Four payoff indicators and personalized payoff function are designed considering driving efficiency, safety, and comfort requirements. The advantage of the proposed method is its ability to reduce complexity, improve effectiveness, and consider personalized driving preferences. Five different typical scenarios at unsignalized intersections are simulated, and the results demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Kunyang Cai, Ting Qu, Bingzhao Gao, Hong Chen
Summary: This paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Yongjun Yan, Nan Li, Jinlong Hong, Bingzhao Gao, Jia Zhang, Hong Chen, Jing Sun, Ziyou Song
Summary: This study proposes an eco-coasting strategy that calculates the optimal timing and duration of coasting maneuvers using road information preview. By evaluating different coasting mechanisms, it is found that the engine start/stop method performs better in terms of fuel consumption and travel time. The online performance of the eco-coasting strategy is evaluated using Mixed Integer Model Predictive Control (MIMPC), and simulation results show that it achieves near-optimal performance and outperforms the rule-based method.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Ping Wang, Xiyue Zhang, Jianpeng Shi, Bin Gou, Lin Zhang, Hong Chen, Yunfeng Hu
Summary: This paper proposes an MPC-based control strategy to prevent vehicle rollover during high-speed driving. By considering tire force saturation and real-time rollover index, the control region is divided into different stability regions. State constraints are set based on driver behavior and road conditions to improve overall vehicle performance.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Xiao Hu, Hong Chen, Qiao Ren, Xun Gong, Ping Wang, Yunfeng Hu
Summary: This article proposes a well-developed method for the estimation and expansion of the vehicle stability region by improving the region of attraction (RoA) based on sums of squares (SOS) programming. The method reduces conservatism by formulating an improved SOS program with an optimization objective and a customized algorithm. The computational burden is reduced by using a feasibility prejudgment strategy and a dynamic search range. Simulations and hardware-in-the-loop experiments verify the effectiveness of the method.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Engineering, Civil
Yanjun Huang, Shuo Yang, Liwen Wang, Kang Yuan, Hongyu Zheng, Hong Chen
Summary: This paper proposes an efficient self-evolution method for reinforcement learning algorithms using a combination of SAC and BC. The method improves convergence efficiency without sacrificing the exploration advantage of reinforcement learning.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Yangyang Feng, Shuyou Yu, Encong Sheng, Yongfu Li, Shuming Shi, Jianhua Yu, Hong Chen
Summary: This paper proposes a hierarchical control strategy that considers the longitudinal and lateral coupling property of vehicle platoons. By using a predictive control scheme, the strategy predicts and maintains the vehicles on the designated lanes, avoiding the need to solve nonlinear optimization problems and reducing the computational burden. The joint simulation results demonstrate the effectiveness of the proposed strategy in terms of maintaining the velocity and safety distance of vehicles in both straight and curved road scenarios.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
