Cooperative Adaptive Cruise Control Research Articles

Fuel Consumption Evaluation of Connected Automated Vehicles Under Rear-End Collisions

Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers.

Data generation for connected and automated vehicle tests using deep learning models

For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs’ tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and a hybrid model of variational autoencoder and generative adversarial network (VAE-GAN) for trajectory data generation. These models are capable of learning a compressed representation of the observed data space, and generating data by sampling in the latent space and then mapping back to the original space. The real data and the generated data are applied in the car-following model of CAVs with cooperative adaptive cruise control (CACC) to evaluate safety performance using the time-to-collision (TTC) index. The results indicate that the generated data of the two generative models have reasonable differences while maintaining a certain similarity with the real samples. When real and generated trajectory data are applied to the car-following model of CAVs, the generated trajectory data increases the number of new critical fragments whose TTC is smaller than the threshold. The WGAN-GP model performs better than the VAE-GAN model according to the ratio of critical fragments. Findings of this study provide useful insights for CAVs’ tests and safety performance improvement.

Lane Management Strategies in a Connected Environment: Analysis of Freight Corridor Scenarios for I-710 in Southern California

Connected vehicles (CVs) and their supporting infrastructure are expected to play an important role in the management of traffic congestion. As highway expansion projects are becoming less common, deploying emerging technologies is essential to make the most of the current road infrastructure. Most published studies report that CVs improve traffic performance only marginally. Here, we propose that CVs drive cooperatively with cooperative adaptive cruise control (CACC)-enabled vehicles by designating a CACC lane for periods with high flows of slow-moving heavy-duty drayage trucks. To assess if this approach could help absorb year 2035 projected drayage traffic increases at the Ports of Los Angeles and Long Beach, which is the largest port complex in the U.S.A., we analyze three 2035 scenarios for I-710, a key freeway for freight transportation in Southern California: (1) CACC-enabled vehicles are deployed under mixed traffic conditions; (2) CACC-enabled vehicles are restricted to the first lane (left-most lane); (3) the first lane is reserved for CACC-enabled vehicles, and access is optional. Our results suggest that substantial speed improvements can be obtained, but only when the first lane is CACC reserved with optional access, because this approach creates more platooning opportunities and thus helps maximize the benefits of CACC.

Impact Analysis of Driver Takeover Under a Connected Environment: A Study of the External Effects on Vehicle Conversions

The moment we face cooperative adaptive cruise control (CACC), driver takeover, that is, drivers actively take over vehicles to avoid unknown risks, is a necessary concern in reality, especially with the distrust or low acceptance of automated driving technology in the early stage of CACC development. However, the impact of driver takeover on traffic flow has not been accurately presented, as researchers only focused on the taken-over vehicle and neglected the external effects on surrounding vehicles. Departing from the literature, this study provides a novel external perspective for the impact analysis of driver takeover under a connected environment, that is, it considers the resulting communication heterogeneity and specifies multi-class conversions in the entire traffic flow. To be specific, we subdivide the dynamic process of driver takeover into 10 vehicle conversions considering vehicle degradation and platoon reorganization, then analyze their contributions to traffic stability, emissions, and fuel consumption. What is also innovative is that two forms of driver takeover are first developed in this study, which is determined by whether drivers continue opening the communication device after the takeover. On this basis, this study indicates that driver takeover has the potential to cause greater heterogeneity of the traffic flow in contrast to that in the literature and proposes theoretical models for the changed traffic flow configuration and traffic stability after the takeover. Finally, numerical experiments verify the theoretical results with discussions on multiple factors, for example, vehicle proportion, platoon length, and driving behaviors. The findings extend the external effects of driver takeover on multi-class vehicle conversions in heterogeneity traffic flow and provide a more realistic basis for the evaluation of driver takeover.

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.

Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

This paper investigates cooperative adaptive cruise control (CACC) for mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). This research is critical because the penetration rate of AVs in the transportation system will remain unsaturated for a long time. Uncertainties and randomness are prevalent in human driving behaviours and highly affect the platoon safety and stability, which need to be considered in the CACC design. A further challenge is the difficulty to know the exact models of the HVs and the exact powertrain parameters of both AVs and HVs. To address these challenges, this paper proposes a data-driven model predictive control (MPC) that does not need the exact models of HVs or powertrain parameters. The MPC design adopts the technique of data-driven reachability to predict the future trajectory of the mixed platoon within a given horizon based on noisy vehicle measurements. Compared to the classic adaptive cruise control (ACC) and existing data-driven adaptive dynamic programming (ADP), the proposed MPC ensures satisfaction of constraints such as acceleration limit and safe inter-vehicular gap. With this salient feature, the proposed MPC has provably guarantee in establishing a safe and robustly stable mixed platoon despite of the velocity changes of the leading vehicle. The efficacy and advantage of the proposed MPC are verified through comparison with the classic ACC and data-driven ADP methods on both small and large mixed platoons.

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

AbstractCooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

Centralized model‐predictive cooperative and adaptive cruise control of automated vehicle platoons in urban traffic environments

AbstractRecent research has demonstrated that cooperative and automated driving functions can improve traffic efficiency and safety. Traffic efficiency can be increased and the overall vehicle energy consumption can be reduced through cooperative driving functions not only on the highway, but also in urban areas. To this end, this paper presents a linear model predictive control (MPC) algorithm for the optimization of velocity trajectories of vehicles in a platoon in an urban traffic environment. A novel centralized approach is implemented to optimize the total energy consumption of all vehicles while improving traffic efficiency. To solve the optimal control problem, for the first time, an additional data‐driven velocity prediction model of the vehicle in front of the platoon is used to consider the influence of realistic prediction errors when evaluating the developed control strategy in a vehicle simulation environment. The simulated total energy consumption is compared with a rule‐based cooperative adaptive cruise control (CACC) algorithm. Furthermore, the traffic density and the platoon string stability, the robustness and computer time of the centralized approach are also included in the evaluation. Simulation results indicate that, considering all evaluation criteria, the centralized cooperative control strategy can improve energy efficiency by up to 15% in the investigated urban scenario.

Traffic Flow State Analysis Considering Driver Response Time and V2V Communication Delay in Heterogeneous Traffic Environment

In order to study the heterogeneous traffic environment generated by connected automated vehicles (CAVs) and human-driven vehicles (HDVs), the car-following model and basic graph model of the mixed traffic flows of connected automated vehicles and human-driven vehicles are constructed. Considering driver response time and the communication delay of the connected automated vehicles control system, the three-parameter variation law of traffic flow is summarized to solve the traffic congestion problem in heterogeneous traffic environments. Firstly, the probability of six scenarios in queues of cooperative adaptive cruise control (CACC) vehicles, adaptive cruise control (ACC) vehicles, and human-driven vehicles in heterogeneous traffic environments is analyzed. The car-following model is defined, and the parameters are calibrated, and then a fundamental diagram model of traffic flow balance is derived. On this basis, considering driver response time and the communication delay of the linear controller, a car-following model considering multi-party delay is updated and established, and the heterogeneous traffic flow analysis of the two types of delays in the model is carried out. Finally, the microscopic simulation environment is constructed based on SUMO 1.17.0 (Simulation of Urban Mobility) software. The results show that when the permeability (the proportion of connected automated vehicles in a traffic stream) exceeds 0.6, CAVs account for the main part in the heterogeneous traffic, which has a positive impact on the maximum flow and the optimal density and can effectively improve the maximum capacity of the road. The simulation results show that the updated car-following model is reasonable and accurate in dealing with driver response time and V2V communication delay.

Reinforcement Learning-Based Approach for Minimizing Energy Loss of Driving Platoon Decisions.

Reinforcement learning (RL) methods for energy saving and greening have recently appeared in the field of autonomous driving. In inter-vehicle communication (IVC), a feasible and increasingly popular research direction of RL is to obtain the optimal action decision of agents in a special environment. This paper presents the application of reinforcement learning in the vehicle communication simulation framework (Veins). In this research, we explore the application of reinforcement learning algorithms in a green cooperative adaptive cruise control (CACC) platoon. Our aim is to train member vehicles to react appropriately in the event of a severe collision involving the leading vehicle. We seek to reduce collision damage and optimize energy consumption by encouraging behavior that conforms to the platoon's environmentally friendly aim. Our study provides insight into the potential benefits of using reinforcement learning algorithms to improve the safety and efficiency of CACC platoons while promoting sustainable transportation. The policy gradient algorithm used in this paper has good convergence in the calculation of the minimum energy consumption problem and the optimal solution of vehicle behavior. In terms of energy consumption metrics, the policy gradient algorithm is used first in the IVC field for training the proposed platoon problem. It is a feasible training decision-planning algorithm for solving the minimization of energy consumption caused by decision making in platoon avoidance behavior.

CACC Simulation Platform Designed for Urban Scenes

As a key technology to alleviate urban congestion, Cooperative Adaptive Cruise Control (CACC) requires testing and evaluation before being commercialized. To evaluate CACC, simulation plays a key role. However, conventional simulation platforms focus more on highway scenes but less on urban scenes. Hence, this paper proposes a CACC simulation platform for urban scenes. The platform bears the following features: i) able to plug in production Connected and Automated Vehicle (CAV) control systems; ii) able to capture the interaction between Human-driven Vehicles (HVs) and CAVs; iii) able to evaluate CACC with regard to various traffic control; iv) capable of quantifying CACC safety in urban scenes; v) capable of assessing the impact of CACC on mobility in urban scenes. The platform is validated by comparing against actual field tests and previously published theoretical studies. Its credibility is confirmed in terms of free flow CACC, interrupted CACC flow and free flow Partially Connected and Automated Traffic (PCAT). Through the proposed platform, evaluations on CACC safety and mobility are conducted. The results reveal that current CACC technology requires significant enhancement, since CACC technology may interrupt the traffic in urban areas due to platoon splitting, interaction with lane-changing vehicles, etc. Rushing the deployment of current CACC technology in urban areas is probably not a good idea.

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.

Multianticipative Adaptive Cruise Control Compared With Connectivity-Enhanced Solutions: Simulation-Based Investigation in Mixed Traffic Platoons

The paper explores via simulation analysis how multianticipative adaptive cruise control (M-ACC) can address traffic oscillations with respect to cooperative adaptive cruise control (CACC) approaches. The work is grounded on our previous findings where we suggested a functioning logic and a performance characterization of M-ACC. That activity followed a manufacturer’s claim that vehicles capable of reacting to more than one leader ahead are already populating public roads. In this light, M-ACC represents an intermediate solution between the traditional ACC, which typically is capable of reacting to only one vehicle ahead, and connectivity-enhanced approaches, which in principle could track multiple leaders. Given that M-ACC is effective from small penetration rates—as it does not require the presence of other M-ACC-equipped vehicles to reach its full potential—performance comparison with CACC is not straightforward. In particular, for a fair comparison it is necessary to quantify the performance gap as a function of the penetration rate, which is exactly the objective of this paper. Three CACC topologies are investigated and both stochastic simulations and Pareto analyses are performed. The results demonstrate that, for small market penetration figures, M-ACC can still guarantee better performances than any CACC heterogenous platoons for all the metrics considered given that it does not require infrastructure or other adjacent vehicles featuring the same technology. Considering that the market adoption of connectivity-based solutions is facing challenging practical issues, M-ACC could represent a suitable as well as realistic option to harvest the benefits of automation to traffic flow in the near future.

Hierarchical cooperative eco‐driving control for connected autonomous vehicle platoon at signalized intersections

AbstractVehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies.

Stability Analysis of Mixed Traffic Flow Considering Personal Space under the Connected and Automated Environment

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

Cooperative Adaptive Cruise Control for Connected Autonomous Vehicles Using Spring Damping Energy Model

Cooperative adaptive cruise control (CACC) has been widely considered as a potential solution for reducing traffic congestion, increasing road capacity, reducing fossil fuel consumption and improving traffic safety. Traditional CCAC methods rely heavily on the vehicle-to-vehicle communications to achieve cooperation. However, in the real-world scenarios, unreliable communication will degrade CACC to adaptive cruise control, which may bring negative influences on safety (i.e., increase the risk of collisions). To overcome this drawback, this paper innovatively applies a spring damping energy model to construct a robust autonomous vehicle platoon system. The proposed design of the energy model ensures that the stability and safety of the platoon system be maintained in the event of such sudden degradation. Based on this technique, a distributed control protocol which only utilizes local information from neighbors is then proposed. Furthermore, some practical constraints such as the connectivity of the vehicle platoon system and the bound of the control inputs are guaranteed. Finally, the effectiveness of the proposed CCAC strategy is validated by multiple simulation experiments in Unreal Engine.

Ecological Cooperative Adaptive Cruise Control for Heterogenous Vehicle Platoons Subject to Time Delays and Input Saturations

Public concerns about energy crisis and environmental issues lead to higher fuel economy standards and more stringent limitations on greenhouse gas emissions for ground vehicles, and ecological cooperative adaptive cruise control (eco-CACC) is considered to be effective in reducing fuel consumption and greenhouse gas emissions of vehicle platoons. In this paper, an eco-CACC strategy is presented for heterogeneous platoons with time delays and input saturations to achieve platoon stability, fuel economy, riding comfort and driving efficiency. The proposed eco-CACC strategy includes distributed linear feedback control (DLFC) for following vehicles and model predictive control (MPC) for the leading one. To obtain platoon stability, the DLFC protocol is designed using state errors between an ego vehicle and its preceding one, and the frequency domain method is employed to derive the sufficient conditions of platoon stability for the DLFC protocol. Then, to achieve fuel economy, passenger comfort and driving efficiency without violating the different input saturations of vehicles, the constrained fuel consumption optimization problem of the leading vehicle (CFCO-LV) is formulated based on delay MPC, where the weighted sum of the overall fuel consumption and velocity band-stop functions of vehicle platoon is minimized. To quickly obtain the MPC controller, an improved particle swarm optimization (PSO) algorithm is used to solve the CFCO-LV problem. Simulations are implemented to validate the effectiveness of the proposed eco-CACC strategy, and the simulation results demonstrate that, compared with benchmark, the proposed strategy can save 2.19%~8.24% of fuel for the heterogeneous platoon under different velocity ranges.

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.