Cooperative Adaptive Cruise Control System Research Articles

Investigation of Different Communication Topologies for Cooperative Adaptive Cruise Control Systems

After Adaptive Cruise Control (ACC) system applications, Cooperative Adaptive Cruise Control (CACC) systems are becoming an important part of automotive technology and industry in autonomous vehicles convoy applications. Together with this developing technology, CACC systems use vehicle to vehicle (V2V) communication to automatically transmit the movement information of vehicles. In this context, ACC systems use Radar or LIDAR measurements while CACC systems also consider the acceleration of the preceding vehicle. In this paper, the forms of information transmission between vehicles in autonomous vehicle convoys using CACC systems have been examined. From these forms of information transmission, the leader following, the predecessor follow-ing and the leader – predecessor following topologies have been considered. For each topology, an autonomous vehicle convoy consisting of eight vehicles was modeled in the MATLAB/Simulink environment. The feedforward and the feedback control system structure were given for CACC and ACC systems. For different communication topologies, the position-time, the speed-time, the acceleration-time and the headway time-time results were obtained. The maximum intervehicle distance error plots for each vehicle in different topology convoys were given to analyze the dynamic behavior of the convoys. The results have been analyzed in terms of the maximum intervehicle distance, the maximum speed, the minimum and the maximum acceleration, and the maximum headway time deviation from the desired headway time.

Sampled-Data Cooperative Adaptive Cruise Control for String-Stable Vehicle Platooning with Communication Delays: A Linear Matrix Inequality Approach

This study aims to propose a sampled-data control technique, utilizing a linear matrix inequality (LMI) approach, to achieve string-stable vehicle platooning in a cooperative adaptive cruise control (CACC) system with communication delays. To do this, a decentralized sampled-data controller design technique that combines one controller using sensor measurements and another one utilizing vehicle-to-vehicle (V2V) communication, ensuring both individual and string stability, is proposed first. Next, a memory sampled-data control (MSC) approach is presented to account for transmission delays in V2V communication. Additionally, an improved Lyapunov–Krasovskii functional (LKF) is presented to improve computational complexity and sampling performance. The design conditions are formulated as linear matrix inequalities (LMIs) in the time domain, facilitating efficient stability analysis and optimization. Finally, vehicle platooning simulations are provided to validate the effectiveness and feasibility of the proposed technique.

A cooperative interaction strategy for vehicle platoons to obtain merging gaps in connected environments

Vehicle platoons can significantly improve traffic throughput and reduce fuel consumption and emissions. In the formation of platoons, it is crucial to generate safe merging gaps. This process requires appropriate cooperative management and control strategies. This study proposes a cooperative interaction strategy for vehicle platoons, including a communication management system and vehicle control strategies. These strategies can reduce velocity fluctuation in the process of gap generation and improve traffic performance. First, a communication management system within a platoon was developed, according to the standard communication protocol (SAE J2735), ensuring that external vehicles can join the platoon efficiently and orderly. Next, a cooperative adaptive cruise control (CACC) system was designed, which adopts feedforward and feedback control. Furthermore, the influence of increasing gaps on the stability of the platoon was considered. A cooperative control strategy for a virtual guiding vehicle (VGV) was introduced to switch the following target and linearly change the distance input of the controller. In this way, the downstream vehicles were guided to smoothly generate a safe merging gap, which can reduce speed fluctuation, and ensure the stability and safety of the platoon. Finally, the entire process of interaction in a vehicle platoon was tested in a simulation environment. The results showed that, compared with the parameter adaptive control strategy, the maximum velocity overshoot of the platoon vehicles was reduced by 56%, recovery stabilization time was reduced by 47%, and vehicle jitter was reduced by 43%. The driving security and platoon stability were both within the control boundaries set for evaluation.

Car-following stability improvement of cooperative adaptive cruise control based on distributed model predictive control

To solve the problem of large fluctuation of vehicle following distance in cooperative adaptive cruise control (CACC), a distributed model predictive control (DMPC) strategy is proposed. The idea of hierarchical control is performed to control the CACC system. The controller is divided into an upper controller and a lower controller. The upper controller calculates the expected acceleration of the vehicle according to the platooning state, and the lower controller controls the throttle and braking system pressure of the vehicle according to the expected acceleration. Firstly, the longitudinal dynamic model of vehicle platooning is established. Secondly, the objective function is designed according to the control objectives, so that the platooning can obtain the optimal control quantity at the current time. Meanwhile, the robust design is used to improve the controller performance, and the optimization of reference trajectory and the extension of feasible domain are used to improve the stability of the controller. Car-following Stability therefore can be improved. Then the lower controller is designed based on a reverse engine model and a reverse braking model. Finally, the effectiveness of the designed control strategy is verified by the co-simulation of Carsim and MATLAB/Simulink. The results show that DMPC can reduce the peak value, the standard deviation, and the root mean square of vehicle following distance error and improve the following stability.

A Deep Time Delay Filter for Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) is a smart transportation solution to alleviate traffic congestion and enhance road safety. The performance of CACC systems can be remarkably affected by communication time delays, and traditional control methods often compromise control performance by adjusting control gains to maintain system stability. In this paper, we present a study on the stability of a CACC system in the presence of time delays and highlight the trade-off between control performance and tuning controller gains to address increasing delays. We propose a novel approach incorporating a neural network module called the deep time delay filter (DTDF) to overcome this limitation. The DTDF leverages the assumption that time delays primarily originate from the communication layer of the CACC network, which can be subject to adversarial delays of varying magnitudes. By considering time-delayed versions of the car states and predicting the present (un-delayed) states, the DTDF compensates for the effects of communication delays. The proposed approach combines classical control techniques with machine learning, offering a hybrid control system that excels in explainability and robustness to unknown parameters. We conduct comprehensive experiments using various deep-learning architectures to train and evaluate the DTDF models. Our experiments utilize a robot platform consisting of MATLAB, Simulink, the Optitrack motion capture system, and the Qbot2e robots. Through these experiments, we demonstrate that when appropriately trained, our system can effectively mitigate the adverse effects of constant time delays and outperforms a traditional CACC baseline in control performance. This experimental comparison, to the best of the author’s knowledge, is the first of its kind in the context of a hybrid machine learning CACC system. We thoroughly explore initial conditions and range policy parameters to evaluate our system under various experimental scenarios. By providing detailed insights and experimental results, we aim to contribute to the advancement of CACC research and highlight the potential of hybrid machine learning approaches in improving the performance and reliability of CACC systems.

Collaborative adaptive cruise control and energy management strategy for extended-range electric logistics van platoon

This paper improves the economy of the extended-range electric logistics van (ERELV) platoon from two aspects of cooperative adaptive cruise control (CACC) and energy management strategy (EMS). Based on the vehicle-to-everything (V2X) communication, to improve the economy of heterogeneous vehicle platoon CACC system, a distributed model predictive controller (DMPC) with stability, comfort, and the economy as optimization goals are designed. The sufficient conditions for the asymptotic stability of the vehicle platoon closed-loop system are obtained by Lyapunov stability analysis. The multi-agent deep reinforcement learning (MADRL) algorithm is used to solve the EMS of the ERELV platoon. Under the framework of centralized training distributed execution (CTDE), the experience of all agents can be obtained during training, and the actions can be output only according to their local observation states during execution. The simulation results show that the designed ecological cooperative adaptive cruise control (Eco-CACC) effectively balances the stability and economy of a heterogeneous vehicle platoon. Taking dynamic programming (DP) as the benchmark, compared with the single-agent algorithm, EMS based on a multi-agent deep deterministic strategy gradient (MADDPG) algorithm can achieve a near-optimal solution while significantly improving the learning efficiency.

A nonlinear disturbance observer based super twisting control for cooperative adaptive cruise control system affected by communication imperfections

A cooperative adaptive cruise control system utilizes vehicle-to-vehicle communication in addition to onboard sensors. Vehicle-to-vehicle communication is established by a short-range wireless network, which is susceptible to communication delay or loss of communication frequently. It would result in higher inter-vehicle distance to maintain the string stability of a platoon. In this paper, a nonlinear observer-based super twisting control is proposed to retain the property of string stability in various practical scenarios of communication delay, complete loss of communication, and parametric uncertainty. It ensures asymptotic convergence of the spacing error. The stability of an individual vehicle and the string stability of a platoon is derived. The performance of the proposed scheme is compared with two well-known methods for controlling a platoon of cooperative adaptive cruise control and adaptive cruise control vehicles.

Safety Spacing Policy and Cruise Control Strategy for Heterogeneous Platoon to Enhance Safety and Efficiency

Automated platooning is gaining increasing attention because it is regarded as a highly efficient transportation mode to solve traffic congestion problem. Although different intraplatoon spacing strategies have been proposed, little research has focused on quantitative optimization of spacing policy from a safety perspective considering differences between automated and human-driven platooning. In addition, there still is a lack of comprehensive research into string stability and delay robustness of cooperative adaptive cruise control (CACC) for heterogeneous platoons. This paper proposes a quantitative spacing policy in quadratic form based on safety index optimization under hard braking scenarios. The expected value of collision severity is taken as the main index for spacing optimization. The CACC strategy then tracks the proposed spacing considering fluctuation of multitime delays and uncertainty of vehicle dynamics. Based on sliding model control theory and frequency stability criteria, the sufficient conditions satisfying CACC stability and string stability were given. The robustness of the CACC system considering communication latency fluctuation was shown using simulation results. Compared with the existing control strategy, the proposed control strategy is more robust to communication latency, and string stability can be guaranteed. The proposed spacing strategy effectively can reduce collision risk, and the proposed CACC strategy improves latency robustness on the premise of ensuring string stability.

Cooperative adaptive cruise control system for electric vehicles through a predictive deep reinforcement learning approach

In this paper, a predictive deep reinforcement learning-based control algorithm is introduced for the cooperative adaptive cruise control (CACC) system. The proposed control algorithm achieves an optimal response in the interaction of the host vehicle with the leading vehicles. For this purpose, at first, a new algorithm based on deep neural networks is proposed for modeling and predicting the driving behavior of the leading vehicle, according to the traffic behavior of its surrounding vehicles. Then, the outputs of the predictive model are used as additional states for the upper-level controller of the CACC system, which is designed based on a deep reinforcement learning approach. Therefore, the proposed controller uses conventional states such as relative distance and speed of the host vehicle along with the leading vehicles and employs the speed profile of the leading vehicle in the future time horizons to train the reinforcement learning agent. Thus, the upper-level controller learning process is improved. In addition, for regulating the lower-level controller of CACC system against parametric uncertainties in electric vehicle dynamics, a deep reinforcement learning-based controller is proposed, which can be retuned according to the real-time estimated parameters of vehicle mass and road grade. Simulation results represent the promising performance of the proposed control algorithm for maintaining a balance between travel safety, passenger comfort, and reducing vehicle energy consumption.

An Adaptive Traffic-Flow Management System with a Cooperative Transitional Maneuver for Vehicular Platoons

Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions.

Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Longitudinal vehicle control models, such as Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), have been the core component of many Automated Driving Systems (ADS) or Advanced Driver Assistance System (ADAS). ACC and CACC systems make vehicles drive with faster reaction time and smaller following distance than manual vehicles, alleviating the congestions and attenuating traffic disturbances. Many existing ACC and CACC control models focus on three strategies that can indirectly mitigate congestions and damp traffic shockwaves: time headway suppression, stability improvement, and adaptive mode-switching control based on traffic conditions. Although congestion mitigation and shockwave damping are achievable with those control strategies, the improvement is often limited to the by-product effect of their main control objectives of headway suppression and system stability. Many adaptive models also need to calibrate many parameters for different road conditions.This paper proposes a new ACC and CACC framework that directly integrates the congestion shockwave damping and traffic congestion mitigation into the objective function of a dynamic control framework. A Model Predictive Control (MPC) based dynamic optimization framework is proposed to balance the trade-offs among multiple objectives, including safety, efficiency, shockwave, elasticity, and driver’s comfort. The proposed framework is applied in both ACC and CACC platoons mixed with manual vehicles, and five different control models are explored. The stability conditions of the proposed models are derived analytically and analyzed numerically. Three experimental studies, including platoon, ring-road, and traffic simulation studies, indicate promising results of proposed models in reducing shockwave propagation speed and mitigating traffic congestion under different environments compared with existing ACC and CACC models.

Memory-Anticipation Strategy to Compensate for Communication and Actuation Delays for Strings-Stable Platooning

Cooperative Adaptive Cruise Control (CACC) has the potential to significantly improve traffic throughput by reducing inter-vehicle following gap. However, communication and actuation delays in CACC systems are detrimental to string stability. A much larger time gap than the lump sum of the two delays is often required for CACC to guarantee string stability, which compromises their benefits on traffic throughput. It remains as a challenge to design string-stable CACC controllers that operate at small time gaps under delays. In this paper, we design a memory-anticipation CACC to explicitly handle communication and actuation delays to fulfill string stability specifications. The proposed controller employs a novel memory-anticipation spacing policy to address communication and actuation delays and is applicable to heterogeneous platoons. Unlike existing CACC controllers, our proposal utilizes past information of the preceding vehicle (i.e. the memory) instead of its current state/control information, which makes the controller robust to communication delay. Furthermore, a Smith predictor (i.e. the anticipation) is designed to compensate for the actuation delay. Both individual vehicle stability and string stability can be guaranteed with the minimum time gap as small as the summation of communication delay and actuation delay. Controller analysis and simulation verify the enhancement of the stability performance with respect to existing studies.

An Evolutionary Learning Framework of Lane-Changing Control for Autonomous Vehicles at Freeway Off-Ramps

This paper proposes a lateral control strategy for autonomous vehicles (AVs) and develops an evolutionary learning framework for off-ramps. Random forest (RF) and back-propagation neural network (BPNN) integrated with model predictive control (MPC) algorithm are respectively used to capture the decision-making and trajectory characteristics during the lane-changing maneuver based on the Next Generation Simulation (NGSIM) dataset. Then, a running cost function is calculated to optimize the trajectory dataset. Finally, the numerical simulation is conducted to investigate the characteristics of the proposed framework. Simulation results indicate that the performance of our method is much better than some other methods in lane-changing gap choice and trajectory execution. Moreover, the traffic system controlled by the evolutionary algorithms reaches the highest capacity and safest level when all vehicles are equipped with cooperative adaptive cruise control (CACC) systems. On the contrary, the scenario with 50% CACC vehicles shows the lowest travel efficiency and the worst safety because of the CACC vehicles’ degradation. Furthermore, three iterations and 500 vehicle trajectories at each optimization cycle are recommended for the application in the off-ramp traffic control.

On multi-rate prediction governor for CACC system with sensor errors and communication delays

Among automated driving technologies, ACC (Adaptive Cruise Control), which controls the distance between vehicles and relative speed, is becoming popular. CACC (Cooperative ACC), which is an advanced ACC system, uses V2V communication and its sensors to control the distance between vehicles. Sensor errors and communication delays have a significant impact on control performance of CACC. In this study, error correction of CACC is performed using accurate information with delay. A noise shaping feedforward controller called a predictive governor is used for this purpose. When applying CACC system to the predictive governor, we cannot design the stable prediction governor due to the relative order or zero characteristics of the CACC system. Therefore, this paper proposes multi-rate type prediction governor that can be applied to the CACC system. The results show that the use of multi-rate enables the application of the prediction governor to CACC systems, and more accurate temporary stopping is achieved with sensor noise and delayed communication.

Implicit Lyapunov Function-based Formation Control for Heterogeneous Vehicular Platoons with Time Delays

In order to lower the drivers’ driving pressure and improve traffic efficiency, academic and industrial interest in the cooperative adaptive cruise control (CACC) system has been substantial. In this research, we use the Implicit Lyapunov Function (ILF) technique to develop a switching controller for HVPs with temporal delays. The constant time headway strategy is utilized to construct the HVPs error model first. Then, the implicit Lyapunov function is used to develop the switching controller. Finally, the conditions of internal stability and string stability are obtained. The suggested switching controller has been shown to maintain internal and string stability in simulations involving heterogeneous vehicle platoons.

Comparing the Observable Response Times of ACC and CACC Systems

This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits.

Robust Non-Fragile Fault Tolerant Control for Ensuring the Safety of the Intended Functionality of Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) has the potential to significantly improve road safety, highway throughput and reduce fuel consumption. However, the safety of the intended functionality (SOTIF) of CACC that focuses on unreasonable risks related to performance limitations has not been addressed. Moreover, various unknown uncertainties, disturbances, and controller perturbations present in the road environment make the guarantee of SOTIF for CACC a more challenging problem. This study presents a robust non-fragile fault tolerant control (RNFTC) strategy as a quantitative risk reduction method for ensuring SOTIF of CACC with system uncertainty, multisource disturbances, and controller perturbations. First, an intermediate based robust estimation method is proposed to estimate the performance limitations of perception and actuation, system states, and matched disturbances, simultaneously. Second, a robust non-fragile <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{\infty }$ </tex-math></inline-formula> FTC method is proposed to accommodate the simultaneous presence of performance limitations and disturbances. Third, theorems for solving optimal estimator and controller gains of the proposed RNFTC are derived in terms of linear matrix inequalities (LMIs). The requirements for CACC system stability, robustness, non-fragile and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{H}_{\infty }$ </tex-math></inline-formula> performances under the proposed RNFTC are analyzed using Lyapunov theory. Finally, a series of comparative simulations with the CACC system are conducted to demonstrate the effectiveness and superiority of the proposed RNFTC method on ensuring SOTIF of CACC under unknown uncertainties, disturbances, and perturbations.

Encrypted Transfer of Traffic Sign Information for Advanced Driving Assistance Systems Using Invisible Security Patches

Adversarial attacks pose serious traffic safety risks that could potentially impede the deployment of automated driving systems (ADS). Recent discoveries show that imperceptible perturbations (also referred to as adversarial attacks) to inputs of deep neural networks employed in these systems could impair their image recognition and prediction capabilities. Adversarial attacks can misguide advanced driving assistance systems (ADAS) to misclassify a traffic sign (e.g., classify a stop sign as a speed limit sign), causing the vehicle to accelerate instead of stop, which may potentially result in catastrophic incidents. This paper investigates encrypted invisible security patches placed on traffic signs and an onboard machine-vision system that can be integrated into the ADAS (i.e., ADS and cooperative adaptive cruise control systems). The security patches are essentially a visual representation of encrypted hash values generated via a one-way cryptographic algorithm. In this way, every traffic sign will be associated with a unique security pattern that can only be recognized by the proposed machine-vision technology. Additionally, these patches will be made invisible with the help of ultraviolet reflective sign technology. The proposed methodology aims to bring an additional security layer to sign recognition to enhance the reliability of ADAS. If a traffic sign is physically altered to attempt an adversarial threat, the security patch on the sign will not match with the sign information, and the driver will receive a warning to take control of the driving. For self-driving vehicles, a signal will be sent to the autonomous vehicle to enforce a safe driving action.

Simulation-Based Analysis of "What-If" Scenarios with Connected and Automated Vehicles Navigating Roundabouts.

Despite the potential of connected and automated vehicles (CAVs), there are still many open questions on how road capacity can be influenced and what methods can be used to assess its expected benefits in the progressive transition towards fully cooperative driving. This paper contributes to a better understanding of the benefits of CAV technologies by investigating mobility-related issues of automated vehicles operating with a cooperative adaptive cruise control system on roundabout efficiency using microscopic traffic simulation. The availability of the adjustment factors for CAVs provided by the 2022 Highway Capacity Manual allowed to adjust the entry capacity equations to reflect the presence of CAVs on roundabouts. Two mechanisms of entry maneuver based on the entry lane type were examined to compare the capacity target values with the simulated capacities. The microscopic traffic simulator Aimsun Next has been of great help in building the “what-if” traffic scenarios that we analysed to endorse hypothesis on the model parameters which affect the CAVs’ capabilities to increase roundabouts’ throughput. The results highlighted that the increasing penetration rates of CAVs have greater impacts on the operational performances of roundabouts, and provided a synthetic insight to assess the potential benefits of CAVs from an efficiency perspective.

Stability Analysis of Heterogeneous Traffic Flow with Connected and Automated Vehicles: Joint Consideration of Communication Failures and Driver Takeover

As a primary stage of connected and automated vehicles (CAVs), the cooperative adaptive cruise control (CACC) system takes some outstanding advantages such as accurate perception and timely reaction, which benefit from vehicular communication, over human-driven vehicles (HDVs). However, these advantages will turn into stability risks in case of communication failures caused by malicious attacks. Thus, this study aims at analyzing the stability of heterogeneous traffic flow with communication failures. We model two types of communication failures: bogus messages and transmission delay, and introduce drivers’ takeovers to react to communication failures. As a result, heterogeneous traffic flow consists of normal CACC vehicles, CACC vehicles with communication failures, and HDVs. Then, a series of numerical analyses, including startup and braking analysis, incidents analysis, and density wave, are proposed to verify the theoretical models and demonstrate their major properties. Besides, a discussion on traffic capacity is presented to analyze the overall impact of communication failures on traffic flow characteristics. The findings can help to investigate the stability evolution of heterogeneous traffic flow and determine the appropriate traffic flow configuration under communication failures.