Cooperative Adaptive Cruise Control Controller Research Articles

Synthesis of Cooperative Adaptive Cruise Control With Feedforward Strategies

Cooperative adaptive cruise control (CACC) technique enhances the coordination of vehicles on road. Through CACC controllers, the platoon can exhibit promising traffic performance. In this paper, we study the control synthesis for two types of CACC systems, one of which uses acceleration feedforward while the other uses control feedforward. Under the predecessor-following topology, a general state-space model of vehicle platoon is established and the CACC characteristics including stabilization and string stability are realized by a set of constraints. To relax computation, complete quadratic Lyapunov-Krasovskii functionals are adopted and the CACC controllers are solved by linear matrix inequality tools. The proposed method provides a computationally efficient way to realize CACC design, and comparative experiments with existing work demonstrate the advantages.

Robust Time-Delay Feedback Control of Vehicular CACC Systems with Uncertain Dynamics.

This paper proposes a new, robust time-delay cooperative adaptive cruise control (CACC) approach for vehicle platooning systems with uncertain dynamics and varying communication delay. The uncertain CACC models with perturbed parameters are used to describe the uncertain dynamics of the vehicle platooning system. By combining the constant time headway strategy and predecessor-following communication topology, a set of robust delay feedback controllers is designed for the uncertain vehicle platoon with varying communication delay. Then, the set of CACC controllers is computed by solving some linear matrix inequalities, which further establish the robust (string) stability of the uncertain platooning system with the varying communication delay. The co-simulation experiment of CarSim and Simulink with a group of a seven-car platoons and varying velocity is used to demonstrate the effectiveness of the presented method.

Multi-Model Adaptive Control for CACC Applications

This paper proposes a multi-model adaptive control (MMAC) algorithm based on Youla-Kucera (YK) theory to deal with heterogeneity in cooperative adaptive cruise control (CACC) systems. The main idea of MMAC is to choose the plant in a predefined set that best approximates the system dynamics, applying the corresponding predesigned controller. A set of linear plants describing different vehicle dynamics is defined. Different CACC controllers are designed depending on these linear plants. Simulation and experimental results prove how MMAC determines the closest plant in the set, choosing the CACC system able to ensure string stability.

Cooperative Adaptive Cruise Control of Vehicle Platoons with Fading Signals and Heterogeneous Communication Delays

Abstract This paper proposes a new cooperative adaptive cruise control (CACC) approach of vehicle platoons with fading signals and heterogeneous communication delays. The CACC model with variable input delays is established to describe the varying time-delays from transmitting acceleration of the front vehicle. The fading signal gains may be unknown and uncertain due to heterogeneous V2V wireless channels. Then a set of decentralized time-delay feedback CACC controllers is computed in such way that each vehicle evaluates its own control strategy using only neighborhood information. In order to establish string stability of the platoon with the decentralized controllers, some sufficient conditions are obtained in form of linear matrix inequalities. The scenarios, consisting of seven different cars with heterogeneous wireless channels, are used to demonstrate the effectiveness of the presented method.

Mixing V2V- and non-V2V-equipped vehicles in car following

Cooperative Adaptive Cruise Control (CACC) provides significant traffic flow improvements when a vehicle-to-vehicle (V2V) communication link exists with the preceding vehicle, but it degrades to an Adaptive Cruise Control (ACC) when this communication link is no longer available. This degradation occurs even if the information from another V2V-equipped vehicle ahead (different to the preceding one) is still available. This paper presents a novel car-following control system–Advanced Cooperative Adaptive Cruise Control (ACACC)–that benefits from the existing communication with this vehicle ahead in the string, reducing inter-vehicle gap whereas keeping string stability. The proposed control structure provides a hybrid behaviour between two CACC controllers with different time gaps according to the string position of the vehicle with the V2V communication link available. An stable hybrid behavior between both controllers is ensured through the Youla-Kucera parameterization. Simulation and real experiments show the proper behaviour of the designed control algorithm and a good performance compared to existing ACC/CACC controllers.

Truck CACC Implementation and Test to Verify Control Performance

This paper presents quantitative driving performance results for a cooperative adaptive cruise control (CACC) system tested on Class-8 trucks. It focuses on the vehicle following control performance measured on a closed test track and in mixed traffic on public freeways. The speed and distance tracking errors are reported for the string of three trucks driven under CACC control.

Cooperative Adaptive Cruise Control With Adaptive Kalman Filter Subject to Temporary Communication Loss

Cooperative adaptive cruise control (CACC) communicates the relevant preceding vehicle state data to the follower (ego) vehicle to improve the vehicle following capabilities. In general, the CACC utilizes the preceding vehicle's desired acceleration from wireless communication as a feedforward term in the controller of the ego vehicle, which dominantly determines the total control input. However, communication loss would degrade CACC to adaptive cruise control (ACC), where the lack of the feedforward term during communication loss would increase the inter-vehicular distance or, otherwise, may lead to collision during vehicle emergency braking. This paper proposes a control algorithm with an adaptive Kalman filter estimating the acceleration of a preceding vehicle, and the estimated acceleration is implemented as a feedforward signal in the ego-vehicle CACC controller in case of communication loss. The proposed control algorithm is evaluated by the experiments using mobile robots that emulate driving. In addition, the simulations of real vehicles are also conducted. The results of simulations and robot experiments show that the performance of implementing the adaptive Kalman filter during communication loss is better than fallback to ACC and the normal Kalman filter based on the Singer model.

Modeling and Numerical Analysis on Communication Delay Boundary for CACC String Stability

Co-operative Adaptive Cruise Control (CACC) uses wireless communication between vehicles to form a vehicle platoon. It has been proven that CACC aids in the stability and efficiency of traffic flow, and also reduces the energy consumption of the vehicles by lowering air resistance of the following vehicles. However, communication delay is a natural uncertainty for a platoon under CACC mode. When the delay gets worse to a particular level, the string stability of the platoon will be destroyed, meaning spacing or speed errors will be amplified upstream from vehicle to vehicle in the platoon. Therefore, in this study, a numerical method is proposed to identify the communication delay boundary according to the vehicle dynamics and the parameter settings of the CACC controller. First, in the Laplace domain, a series of string stable bodies under different disturbance frequencies are created based on the string stability criteria. Considering the frequency characteristics of traffic flows, the string stable bodies are then combined into a uniform one. Lastly, with the increase of communication delay, its upper bound will be obtained until the area of string stability totally disappears. Two simulations were conducted to verify the correctness and accuracy of the proposed method. The results indicate that the string stability cannot be guaranteed once the communication delay just exceeds the upper bound. This study can provide an objective boundary for practitioners or engineers, thereby allowing them to determine whether wireless inter-vehicle communication is capable of maintaining a string-stable platoon.

Development of a simulation platform for safety impact analysis considering vehicle dynamics, sensor errors, and communication latencies: Assessing cooperative adaptive cruise control under cyber attack

While safety is one of the most critical contributions of Cooperative Adaptive Cruise Control (CACC), it is impractical to assess such impacts in a real world. Even with simulation, many factors including vehicle dynamics, sensor errors, automated vehicle control algorithms and crash severity need to be properly modeled. In this paper, a simulation platform is proposed which explicitly features: (i) vehicle dynamics; (ii) sensor errors and communication delays; (iii) compatibility with CACC controllers; (iv) state-of-the-art predecessor leader following (PLF) based cooperative adaptive cruise control (CACC) controller; and (v) ability to quantify crash severity and CACC stability. The proposed simulation platform evaluated the CACC performance under normal and cybersecurity attack scenarios using speed variation, headway ratio, and injury probability. The first two measures of effectiveness (MOEs) represent the stability of CACC platoon while the injury probability quantifies the severity of a crash. The proposed platform can evaluate the safety performance of CACC controllers of interest under various paroxysmal or extreme events. It is particularly useful when traditional empirical driver models are not applicable. Such situations include, but are not limited to, cyber-attacks, sensor failures, and heterogeneous traffic conditions. The proposed platform is validated against data collected from real field tests and tested under various cyber-attack scenarios.

Design and Experimental Validation of a Cooperative Adaptive Cruise Control System Based on Supervised Reinforcement Learning

This paper presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics control of cooperative adaptive cruise control (CACC) system. A supervisor network trained by real driving data is incorporated into the actor-critic reinforcement learning approach. In the SRL training process, the actor and critic network are updated under the guidance of the supervisor and the gain scheduler. As a result, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller. The SRL-based control policy is compared with a linear controller in typical driving situations through simulation, and the control policies trained by drivers with different driving styles are compared using a real driving cycle. Furthermore, the proposed control strategy is demonstrated by a real vehicle-following experiment with different time headways. The simulation and experimental results not only validate the effectiveness and adaptability of the SRL-based CACC system, but also show that it can provide natural following performance like human driving.

Cooperative Adaptive Cruise Control for String Stable Mixed Traffic: Benchmark and Human-Centered Design

With the emergence of vehicle-to-vehicle communication technology, cooperative adaptive cruise control (CACC) cars can be expected in the near future. In this paper, novel criteria for string stability are proposed for mixed traffic platoons that consist of both automated and manual driving cars. A mixed traffic string stability definition is proposed to guarantee the boundedness of the motion states fluctuation upstream as well as the safety of the entire platoon. With proper arrangements of the platoon sequence as well as mild restrictions on the leading car’s velocity overshoot, the proposed mixed traffic string stability can be realized via some suitable controller design for the automated cars. The benchmark controller with feedback and feedforward path is first used to verify the above results with rigorous proof. Furthermore, a human-centered CACC system is designed to improve both physical and psychological comfort of a driver under the guarantee of mixed traffic string stability. The action-point car-following model is adapted to quantify the driver’s psychological comfort in combination with the quantification of physical comfort based on jerk. A model predictive control-like blending ratio controller is developed to obtain the optimal time headway in the feedback controller for tradeoffs among driver comfort, fuel efficiency, and traffic throughput under string stability constraints. Finally, a seven-car CACC mixed traffic platoon scenario is simulated. Compared with the benchmark CACC controller design, the human-centered CACC design is shown to largely improve the driving comfort.

Truck platooning on uphill grades under cooperative adaptive cruise control (CACC)

This paper examines CACC truck platooning on uphill grades. It was found that the design of CT policy should consider the effects of low crawl speeds on significant upgrades. Three simple solutions, which have different impacts on traffic flow efficiency, are proposed. Furthermore, truck platoons, controlled by a state-of-the-art CACC model, become asymptotically unstable beyond some critical grade. The errors are permanent, suggesting that trucks fail to re-engage after the upgrade. This occurs by complex interactions between the CACC control and the bounded acceleration capabilities of trucks. New control concepts are developed to complement the existing control model and achieve asymptotic (and string) stability. The instability mechanisms and new control concepts are not specific to the control model used.

Truck Platooning on Uphill Grades under Cooperative Adaptive Cruise Control (CACC)

Abstract: This paper examines CACC truck platooning on uphill grades. It was found that the design of CT policy should consider the effects of low crawl speeds on significant upgrades. Three simple solutions, which have different impacts on traffic flow efficiency, are proposed. Furthermore, truck platoons, controlled by a state-of-the-art CACC model, become asymptotically unstable beyond some critical grade. The errors are permanent, suggesting that trucks fail to re-engage after the upgrade. This occurs by complex interactions between the CACC control and the bounded acceleration capabilities of trucks. New control concepts are developed to complement the existing control model and achieve asymptotic (and string) stability. The instability mechanisms and new control concepts are not specific to the control model used.

Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach

This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the present literature on CACC, data-driven feedforward and optimal feedback control policies are developed by global adaptive dynamic programming (GADP). Due to the presence of nonvanishing disturbance, a modified version of GADP is presented. Interestingly, the developed policy is guaranteed to globally stabilize the vehicular platoon system, and is robust to unmeasurable nonvanishing disturbance. Numerical simulation results are presented to validate the effectiveness of the developed approach.

Stability analysis of traffic flow with extended CACC control models**Project supported by the National Natural Science Foundation of China (Grant Nos. 71571107 and 11302110). The Scientific Research Fund of Zhejiang Province, China (Grant Nos. LY15A020007, LY15E080013, and LY16G010003). The Natural Science Foundation of Ningbo City (Grant Nos. 2014A610030 and 2015A610299), the Fund from the Government of the Hong Kong Administrative

To further investigate car-following behaviors in the cooperative adaptive cruise control (CACC) strategy, a comprehensive control system which can handle three traffic conditions to guarantee driving efficiency and safety is designed by using three CACC models. In this control system, some vital comprehensive information, such as multiple preceding cars’ speed differences and headway, variable safety distance (VSD) and time-delay effect on the traffic current and the jamming transition have been investigated via analytical or numerical methods. Local and string stability criterion for the velocity control (VC) model and gap control (GC) model are derived via linear stability theory. Numerical simulations are conducted to study the performance of the simulated traffic flow. The simulation results show that the VC model and GC model can improve driving efficiency and suppress traffic congestion.

A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control

Abstract In the study of introducing PID controller into Cooperative Adaptive Cruise Control (CACC) system, there still exist some problems, such as driving comfort and fuel consumption problems. Also, not making rational use of intelligent algorithm to tune PID controller cannot be overlooked. Hence, this paper presents a way to tune PID controller using improved chaotic ant swarm (CAS) to achieve the goal of improving driving comfort, fuel consumption and reducing the minimum safe distance. CACC controller, composed of PID controller and safety distance model, is erected with joint simulation of PRESCAN and MATLAB/Simulink. This paper presents experiments carried out on vehicle queues at low and high cruising speeds under various kinds of working conditions, including platoon driving, cut-in, cut-out vehicle queue and so on. Additionally, the experiments were conducted, respectively, before and after setting the PID controller. Results based on the data analysis of velocity, acceleration, the scale of throttle and brake power, wheel vertical displacement and longitudinal spacing between the vehicles demonstrate that this paper improved driving comfort at the same time decreased the minimum safety distance and fuel economy.

Handling Cut-In Vehicles in Strings of Cooperative Adaptive Cruise Control Vehicles

Cooperative adaptive cruise control (CACC) systems are a candidate to improve highway capacity by shortening headways and attenuating traffic disturbances. Although encouraging results have been obtained until now, a wide range of traffic circumstances has to be investigated in order to get reliable CACC systems driving on real roads. Among them, handling both vehicle-to-vehicle (V2V) communications-equipped and unequipped vehicles merging into the string of CACC vehicles is a commonly mentioned challenge. In this article, an algorithm for managing the transitions in response to cut-ins from V2V- or non-V2V-equipped vehicles is developed and tested using a string of four CACC vehicles. A CACC controller is implemented in four production Infiniti M56s vehicles and tested in real traffic, where non-V2V-equipped vehicles can cut in. The effects of a vehicle performing a cut-out are also investigated. Then responses to cut-ins by equipped and nonequipped vehicles are simulated for longer strings of vehicles using car-following models for both the production adaptive cruise control (ACC) system and the newly developed CACC controller. Results demonstrate that the CACC system is able to handle cut-in vehicles without causing major perturbations, while also reducing significantly the impact of this maneuver on the following vehicles, improving traffic flow.

Cooperative Adaptive Cruise Control: Network-Aware Analysis of String Stability

In this paper, we consider a Cooperative Adaptive Cruise Control (CACC) system, which regulates intervehicle distances in a vehicle string, for achieving improved traffic flow stability and throughput. Improved performance can be achieved by utilizing information exchange between vehicles through wireless communication in addition to local sensor measurements. However, wireless communication introduces network-induced imperfections, such as transmission delays, due to the limited bandwidth of the network and the fact that multiple nodes are sharing the same medium. Therefore, we approach the design of a CACC system from a Networked Control System (NCS) perspective and present an NCS modeling framework that incorporates the effect of sampling, hold, and network delays that occur due to wireless communication and sampled-data implementation of the CACC controller over this wireless link. Based on this network-aware modeling approach, we develop a technique to study the so-called string stability property of the string, in which vehicles are interconnected by a vehicle following control law and a constant time headway spacing policy. This analysis technique can be used to investigate tradeoffs between CACC performance (string stability) and network specifications (such as delays), which are essential in the multidisciplinary design of CACC controllers. Finally, we demonstrate the validity of the presented framework in practice by experiments performed with CACC-equipped prototype vehicles.

An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication

For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.