车辆间通信有潜力提高紧急驾驶情况下的反应精度。协作自适应巡航控制（CACC）作为自动化应用，能提供最大交通 throughput 和碰撞避免等好处。CACC系统必须足够稳健以应对所有特殊操作，如干扰车辆切入CACC车群或前车急刹车。为解决此问题，本文第一部分提出基于神经网络的切入检测与轨迹预测方案。接着开发了一个概率框架，通过前述切入预测模块的输出计算切入概率。最后设计了一个特定的随机模型预测控制器（SMPC），将此切入概率纳入其中以增强对检测到的危险切入行为的反应。整体系统通过现实驾驶场景（Safety Pilot Model Deployment SPMD）实现并评估其性能。
英文摘要：
Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).
This paper considers a cooperative cruise control problem from a predictive control perspective. Online decision-making is used to be executed during the driving process based on the information obtained from the network. We formalize a synchronization problem approach from a predictive control theory using bargaining games to find an operating agreement between the vehicles. Finally, we test these results in an emulation environment in a hardware-in-the-loop system.
This paper investigates the asymptotical cooperative cruise fault tolerant control problem for multiple high-speed trains consisting of multiple carriages in the presence of actuator faults. A distributed state-fault observer utilizing the structural information of faults is designed to achieve asymptotical estimation of states and faults of each carriage. The observer does not rely on choice of control input, and thus it is separated from controller design. Based on the estimated values of states and faults, a distributed fault tolerance controller is designed to realize asymptotical cooperat…
Autonomous vehicle following systems are playing a decisive role to increase vehicle density on roads by shortening inter-vehicle time gaps. However, disturbance attenuation along a platoon of vehicles, i.e., string stability, is being a challenging task while time gap is getting shorter. In order to guarantee the string stability of a vehicle platoon, a multi-objective $H_{\infty}$ control formulation for adaptive cruise control and cooperative adaptive cruise control structures has been investigated in this paper. The proposed control method solves an optimization problem and achieves a cont…
Cooperative driving, enabled by communication between automated vehicle systems, is expected to significantly contribute to transportation safety and efficiency. Cooperative Adaptive Cruise Control (CACC) and platooning are two of the main cooperative driving applications that are currently under study. These applications offer significant improvements over current advanced driver assistant systems such as adaptive cruise control (ACC). The primary motivation of CACC and Platooning is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. These applica…
This paper studies the value of communicated motion predictions in the longitudinal control of connected automated vehicles (CAVs). We focus on a safe cooperative adaptive cruise control (CACC) design and analyze the value of vehicle-to-vehicle (V2V) communication in the presence of uncertain front vehicle acceleration. The interest in CACC is motivated by the potential improvement in energy consumption and road throughput. In order to quantify this potential, we characterize experimentally the relationship between inter-vehicular gap, vehicle speed, and (reduction of) energy consumption for a…
Cooperative Adaptive Cruise Control (CACC) is a fundamental connected vehicle application that extends Adaptive Cruise Control by exploiting vehicle-to-vehicle (V2V) communication. CACC is a crucial ingredient for numerous autonomous vehicle functionalities including platooning, distributed route management, etc. Unfortunately, malicious V2V communications can subvert CACC, leading to string instability and road accidents. In this paper, we develop a novel resiliency infrastructure, RACCON, for detecting and mitigating V2V attacks on CACC. RACCON uses machine learning to develop an on-board pr…
Cooperative Adaptive Cruise Control (CACC) systems are considered as key potential enablers to improve driving safety and traffic efficiency. They allow for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time headway (CTH) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance headway (CDH) spacing policies provide superior potential to increase traffic capacity than CTH, a major drawback is a smaller safety margin at high velocities …
Cooperative Adaptive Cruise Control with Constant Distance Gaps for Retrofitted Legacy Vehicles
Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa（比萨大学能源、系统、领土与建筑工程系）；Fraunhofer Institute FOKUS, Berlin（柏林弗劳恩霍夫FOKUS研究所）；Dyson School of Design Engineering, Imperial College London（帝国理工学院戴森设计工程学院）
Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and…
Connectivity in ground vehicles allows vehicles to share crucial vehicle data, such as vehicle acceleration, with each other. Using sensors such as cameras, radars and lidars, on the other hand, the intravehicular distance between a leader vehicle and a host vehicle can be detected, as well as the relative speed. Cooperative Adaptive Cruise Control (CACC) builds upon ground vehicle connectivity and sensor information to form convoys with automated car following. CACC can also be used to improve fuel economy and mobility performance of vehicles in the said convoy. In this paper, 3 car following…
Platoons of autonomous vehicles are being investigated as a way to increase road capacity and fuel efficiency. Cooperative Adaptive Cruise Control (CACC) is an approach to achieve such platoons, in which vehicles collaborate using wireless communication. While this collaboration improves performance, it also makes the vehicles vulnerable to cyber-attacks. In this paper the performance of a sliding mode observer (SMO) based approach to cyber-attack detection is analysed, considering simultaneous attacks on the communication and local sensors. To this end, the considered cyber-attacks are divide…
Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property of noncooperative formation when AVs adopt typical Adaptive Cruise Control (ACC) strategies.…
Connectivity-enabled automation of distributed control systems allow for better anticipation of system disturbances and better prediction of the effects of actuator limitations on individual agents when incorporating a model. Automated convoy of heavy-duty trucks in the form of platooning is one such application designed to maintain close gaps between trucks to exploit drafting benefits and improve fuel economy, and has traditionally been handled with classically-designed connected and adaptive cruise control (CACC). This paper is motivated by demonstrated limitations of such a control strateg…
Adaptive synchronization protocols for heterogeneous multi-agent network are investigated. The interaction between each of the agents is carried out through a directed graph. We highlight the lack of communication between agents and the presence of uncertainties in each system among the conventional problems that can arise in cooperative networks. Two methodologies are presented to deal with the uncertainties: A strategy based on robust optimal control and a strategy based on neural networks. Likewise, an input estimation methodology is designed to face the disconnection that any agent may pre…
This paper deals with the design of resilient Cooperative Adaptive Cruise Control (CACC) for homogeneous vehicle platoons in which communication is vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as consecutive packet dropouts. We present a controller tuning procedure based on linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks, while guaranteeing performance and string stability. The design procedure returns controller gains and gives a lower bound on the maximum allowable number of successive packet dropouts. A numerical example is employed to…
TU Dortmund University（多特蒙德工业大学）；TU Eindhoven（埃因霍温理工大学）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Polytechnic Institute of Porto（波尔图理工学院）；University of Twente（特温特大学）
In the near future, the development of autonomous driving will get more complex as the vehicles will not only rely on their own sensors but also communicate with other vehicles and the infrastructure to cooperate and improve the driving experience. Towards this, several research areas, such as robotics, communication, and control, are required to collaborate in order to implement future-ready methods. However, each area focuses on the development of its own components first, while the effects the components may have on the whole system are only considered at a later stage. In this work, we int…
OpenConvoy: Universal Platform for Real-World Testing of Cooperative Driving Systems
College of Engineering；Computer Science University of Central Florida Orlando, United States https://orcid.org/0009-0007-1826-373X；College of Electrical；Computer Engineering University of Central Florida Orlando, United States；Computer Science University of Central Florida Orlando, United States
Univ. of Georgia（佐治亚大学）；Univ. of Central Florida（中佛罗里达大学）
Cooperative driving, enabled by Vehicle-to-Everything (V2X) communication, is expected to significantly contribute to the transportation system's safety and efficiency. Cooperative Adaptive Cruise Control (CACC), a major cooperative driving application, has been the subject of many studies in recent years. The primary motivation behind using CACC is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. Since the information flow between cooperative vehicles can significantly affect the dynamics of a platoon, the design and performance of control compo…
Experimentally-Driven Analysis of Stability in Connected Vehicle Platooning: Insights and Control Strategies
Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University（艾尔沃大学电气工程与自动化系）；Department of Electrical and Computer Engineering, School of Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）；FinEst Centre for Smart Cities, Tallinn, Estonia（爱沙尼亚塔林智能城市中心）
Cooperative control of Connected and Autonomous Vehicles (CAVs) promises great benefits for mixed traffic. Most existing research focuses on model-based control strategies, assuming that car-following dynamics of human-driven vehicles are explicitly known. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic. We first present a linearized dynamical model for mixed traffic systems, and investigate its controllability and observability. Based on these control-the…
In the last few years, researchers have applied machine learning strategies in the context of vehicular platoons to increase the safety and efficiency of cooperative transportation. Reinforcement Learning methods have been employed in the longitudinal spacing control of Cooperative Adaptive Cruise Control systems, but to date, none of those studies have addressed problems of disturbance rejection in such scenarios. Characteristics such as uncertain parameters in the model and external interferences may prevent agents from reaching null-spacing errors when traveling at cruising speed. On the ot…
Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents Eco-driving trajectory planning for a platoon of heterogeneous electric vehicles (EVs) in urban environments. The proposed control strategy for the platoon considers energy consumption, mobility and passenger comfort, with which vehicles may pass signalized intersections with no stops. For a given urban route, first, the platoon's leader vehicle employs dyna…
The design of cooperative adaptive cruise control is critical in mixed traffic flow, where connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist. Compared with pure CAVs, the major challenge is how to handle the prediction uncertainty of HDVs, which can cause significant state deviation of CAVs from planned trajectories. In most existing studies, model predictive control (MPC) is utilized to replan CAVs' trajectories to mitigate the deviation at each time step. However, as the replan process is usually conducted by solving an optimization problem with information thr…
ACCESS Linnaeus Centre and the Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology（ACCESS林奈斯中心，电气工程学院自动控制系，瑞典皇家理工学院）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Scania CV AB（斯堪尼亚商用车公司）
Cooperative Adaptive Cruise Control (CACC) is an autonomous vehicle-following technology that allows groups of vehicles on the highway to form in tightly-coupled platoons. This is accomplished by exchanging inter-vehicle data through Vehicle-to-Vehicle (V2V) wireless communication networks. CACC increases traffic throughput and safety, and decreases fuel consumption. However, the surge of vehicle connectivity has brought new security challenges as vehicular networks increasingly serve as new access points for adversaries trying to deteriorate the platooning performance or even cause collisions…
