Article
Engineering, Civil
Marco Mirabilio, Alessio Iovine, Elena De Santis, Maria Domenica Di Benedetto, Giordano Pola
Summary: This paper introduces a human-inspired Adaptive Cruise Control system that utilizes Model Predictive Control and traffic macroscopic information to enhance the passenger experience by reducing traffic congestion and dangerous emissions while improving safety on the roads.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Longlong Zhu, Fazhan Tao, Zhumu Fu, Nan Wang, Baofeng Ji, Yongsheng Dong
Summary: This paper proposes an optimal car-following energy management strategy that combines energy management and adaptive cruise control to improve fuel economy of connected and automated fuel cell/battery hybrid electric vehicles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Automation & Control Systems
Sangjae Bae, Yeojun Kim, Yongkeun Choi, Jacopo Guanetti, Preet Gill, Francesco Borrelli, Scott J. Moura
Summary: This paper examines the mathematical formulation and practical implementation of an ecological adaptive cruise controller (ECO-ACC) with connected infrastructure. The proposed ECO-ACC framework for plug-in hybrid electric vehicle (PHEV) and the demonstration results in a real-world setting show significant improvement in energy efficiency.
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
(2022)
Article
Green & Sustainable Science & Technology
Saeed Vasebi, Yeganeh M. Hayeri
Summary: The transportation sector in the United States is the largest producer of greenhouse gas emissions. New energy-optimal algorithms have been proposed, but studies show they could impact traffic flow negatively. A solution called collective-energy-optimal adaptive cruise control (collective-ACC) is introduced to reduce fuel consumption and emissions effectively and improve traffic flow.
Article
Chemistry, Multidisciplinary
Md Abdus Samad Kamal, Kotaro Hashikura, Tomohisa Hayakawa, Kou Yamada, Jun-ichi Imura
Summary: This paper presents an adaptive cruise control (ACC) system with look-ahead anticipation, which improves driving performance by predicting relative states of the preceding vehicle. Multiple vehicles were evaluated in typical traffic scenarios to examine individual driving behavior and vehicle string stability. Additionally, the influences of a small part of vehicles with the proposed ACC on overall traffic were investigated using a traffic simulator and compared with the performances of overall traffic.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
Chaofeng Pan, Aibao Huang, Jian Wang, Liao Chen, Jun Liang, Weiqi Zhou, Limei Wang, Jufeng Yang
Summary: Adaptive cruise control (ACC) is in line with the current emphasis on safety, energy saving and environmental protection. The Energy-Optimal Adaptive Cruise Control (EACC) strategy based on Model Predictive Control (MPC) algorithm achieves energy optimization for pure electric vehicles, balancing tracking performance and economy under various working conditions.
Article
Psychology, Applied
John G. Gaspar, Cher Carney, Emily Shull, William J. Horrey
Summary: This study examined how drivers with different mental models of adaptive cruise control performed in edge cases, finding that participants with strong mental models responded faster in edge-case situations than those with weak mental models. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2021)
Article
Engineering, Mechanical
ZhiHao Xu, JianHua Li, Feng Xiao, Xu Zhang, ShiXin Song, Da Wang, ChunYang Qi, JianFeng Wang, SiLun Peng
Summary: This study improves the adaptability and fuel economy of vehicles in various environments by establishing a neural network to identify the driving cycles of vehicles, dividing weight intervals according to different driving cycles, and dynamically adjusting the weight values through fuzzy control.
INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY
(2022)
Article
Engineering, Mechanical
Lin Liu, Qiang Zhang, Rui Liu, Xichan Zhu, Zhixiong Ma
Summary: This paper proposed a testing and evaluation method for ACC systems, considering safety and human-like factors, based on human driver behavior characteristics extracted from NDS. The method categorizes test scenarios into safety and human-like categories, analyzes driving behavior characteristics, divides behavior into safe, critical, dangerous, aggressive, and normal categories, and designs baselines and weights for evaluation. The method was applied to test an ACC system, which was found to have a significantly different driving pattern compared to human drivers.
Article
Transportation Science & Technology
Silvia F. Varotto, Celina Mons, Jeroen H. Hogema, Michiel Christoph, Nicole van Nes, Marieke H. Martens
Summary: Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) have the potential to reduce crash rates and traffic congestion. This study examines the factors that influence changes in vehicle control when driving with ACC and LKS. The findings show that drivers are less likely to speed and have a short time gap when using ACC and LKS. Factors such as speed limit, acceleration, and the presence of a slower leader influence driver behavior. The results are valuable for the design of automated vehicles and traffic simulations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Review
Computer Science, Information Systems
Saeed Vasebi, Yeganeh M. Hayeri, Ali Mohammad Saghiri
Summary: The transportation sector and its impact on climate change have been a major focus in the past decade. Energy-optimal vehicle control algorithms, such as adaptive cruise control, have the potential to reduce fuel consumption and environmental impact. These algorithms optimize the speed of vehicles based on various constraints, including safety, stability, and comfort. With a wide range of algorithms available, a comprehensive study is needed due to the diversity of objectives and constraints.
Article
Environmental Studies
Fangwu Ma, Yu Yang, Jiawei Wang, Xinchen Li, Guanpu Wu, Yang Zhao, Liang Wu, Bilin Aksun-Guvenc, Levent Guvenc
Summary: The Eco-CACC proposed in this study combines eco-driving and car-following techniques to minimize energy consumption of automated vehicle platoons. Testing results show that Eco-CACC can significantly improve energy performance compared to manual driving.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Automation & Control Systems
Leopoldo Vite, Luis Juarez, Marco A. Gomez, Sabine Mondie
Summary: We investigate the stabilization problem of an Adaptive Cruise Control (ACC) vehicle platoon with input-delay, proposing a dynamic predictor for input-delay compensation and addressing robustness issues. Each vehicle achieves velocity matching and safe inter-vehicular distance using a proportional-integral controller combined with a dynamic predictor. String stability of the closed-loop system, i.e., the ability to attenuate fluctuations, is analyzed in the frequency domain. Simulations of a platoon of five vehicles demonstrate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Shuangyi Hu, Xuemei Ren, Minlin Wang, Dongdong Zheng
Summary: An adaptive control strategy based on optimal sliding surface is designed for multimotor driving servo systems in this article, which simplifies the optimal control problem of high-order systems and reduces overshoot with an optimal adaptive tracking controller. Additionally, an observer is applied to estimate unmeasurable angular velocities, and a synchronization controller is designed to solve the synchronization problem of multimotor driving servo systems. Simulation and experimental results demonstrate the effectiveness of the proposed control scheme.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Engineering, Electrical & Electronic
Jie Li, Yonggang Liu, Abbas Fotouhi, Xiangyu Wang, Zheng Chen, Yuanjian Zhang, Liang Li
Summary: In this research, a learning-based method is used to achieve satisfactory fuel economy for connected plug-in hybrid electric vehicles (PHEVs) in car-following scenarios. By leveraging a data-driven energy consumption model and considering the nonlinear efficiency characteristics, an advanced ADP scheme is designed for connected PHEVs. The cooperative information is also incorporated to improve fuel economy and driving safety.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shanshan Xie, Jingyue Zheng, Jianqiang Wang
Summary: This paper proposes a method based on drivers' cognition for identifying critical parameters of human-like driving behaviors and develops an interpretable motion planning method to generate diverse human-like behaviors.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Wenfeng Guo, Haotian Cao, Song Zhao, Jianqiang Wang, Binlin Yi, Xiaolin Song
Summary: This paper proposes an optimal driver assistance controller based on non-cooperative game and considering the social behaviors of surrounding vehicles. It effectively assists drivers in cut-in scenarios and addresses various social interactions with surrounding vehicles.
APPLIED MATHEMATICAL MODELLING
(2023)
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
Automation & Control Systems
Qing Xu, Yicong Liu, Jian Pan, Jiawei Wang, Jianqiang Wang, Keqiang Li
Summary: This article proposes a control method called RA-SMT for CAVs to defend against integrity attacks from bounded adversaries. The method ensures the safety and control performance of vehicles in complex traffic scenarios and has been proven effective through simulation and practical validation.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Yuning Wang, Heye Huang, Bo Zhang, Jianqiang Wang
Summary: This paper proposes a differentiated decision-making algorithm to improve the passing capability and efficiency of automated vehicles in complicated traffic environments. The algorithm estimates the behavioural characteristic of pedestrians and integrates it into the intelligent driver model, allowing the ego-vehicle to make differential decisions based on various pedestrian features. Validation and simulation results demonstrate the accuracy of pedestrian feature estimation and the effectiveness of the proposed algorithm in improving passing efficiency under safety and manoeuvrability prerequisite.
IET INTELLIGENT TRANSPORT SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Shanshan Xie, Jiachen Li, Jianqiang Wang
Summary: This study designs a network model based on drivers' cognitive characteristics to predict vehicle trajectories and identifies common issues in predicting vehicle trajectories based on drivers' cognitive characteristics through experiments.
IET INTELLIGENT TRANSPORT SYSTEMS
(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)
