Research Article Optimal Connected Cruise Control Design with Time-Varying Leader Velocity and Delays Lihan Liu , 1,2 Yi Xue, 1 Huamin Chen , 1 Zhuwei Wang , 1 Chao Fang, 1,3 Yang Sun , 1 and Yanhua Sun 1 1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 2 School of Information, Beijing Wuzi University, Beijing 101149, China 3 Purple Mountain Laboratory: Networking, Communications and Security, Nanjing, China Correspondence should be addressed to Huamin Chen; chenhuamin@bjut.edu.cn Received 14 October 2021; Accepted 23 November 2021; Published 10 December 2021 Academic Editor: Shahid Hussain Copyright © 2021 Lihan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With the development of intelligent transportation system (ITS), owing to its flexible connectivity structures and communication network topologies, connected cruise control (CCC), increasing the situation awareness of the autonomous vehicle without redesigning the other vehicles, is an advanced cruise control technology attracted extensive attention. However, due to the uncertain traffic environment and the movement of the connected vehicles, the leader speed is typically highly dynamic. In this paper, taking the uncertain time-varying leading vehicle velocity and communication delays into consideration, an optimal CCC algorithm is proposed for both near-static case and general dynamic control cases. First, the analysis for discrete-time error dynamics model of the longitudinal vehicle platoon is performed. Then, in order to minimize the error between the desired and actual states, a linear quadratic optimization problem is formulated. Subsequently, in near-static control case, an efficient algorithm is proposed to derive the solution of the optimization problem by two steps. Specifically, the online step calculates the optimal control scheme according to the current states and previous control signals, and the off-line step calculates the corresponding control gain through backward recursion. Then, the results are further extended to the general dynamic control case where the leader vehicle moves at an uncertain time-varying velocity. Finally, simulation results verify the effectiveness of the proposed CCC algorithm. 1. Introduction In recent years, the increasing number of vehicles has led to serious problems of congestion, air pollution, and traffic accidents. Intelligent transportation system (ITS), as a sys- tem integrating key technologies in the fields of electronics, computing, sensing, robotics, control, signal processing, and communications, will provide a more coordinated and safer traffic network by reducing driving load, increasing traffic information, and strengthening route management [1–3]. One of the typical applications of ITS is cruise control system, which plays an important role in improving on-road safety by relieving drivers’ burden [4–8]. Connected cruise control (CCC) has been recognized as an emerging cruise control technology that is significantly improving the reliability, stability, and safety of vehicles and has recently attracted considerable attention in both academia and industry [9–11]. In particular, CCC allows the autono- mous vehicle to benefit from V2V information broadcast by multiple vehicles in front without requiring those vehicles to be equipped with sensors and actuators. Human-driven vehi- cles in a platoon only need to broadcast their motion informa- tion to the communication network, without the need to install other equipment. Furthermore, the CCC system has a flexible network topology that allows each vehicle to commu- nicate with nearby members using vehicle-to-vehicle (V2V) communications [12–14]. CCC vehicles can exploit V2V com- munications to collect real-time status information such as the distance, speed, and acceleration of multiple vehicles from vehicles ahead [15, 16]. Then, a suitable control strategy is pro- posed to provide safe and stable driving according to the infor- mation received. In general, there is evidence that CCC is Hindawi Journal of Sensors Volume 2021, Article ID 5961101, 14 pages https://doi.org/10.1155/2021/5961101