Cooperative Adaptive Cruise Control Strategy Research Articles

V2V‐enabled cooperative adaptive cruise control strategy for improving driving safety and travel efficiency of semi‐automated vehicle fleet

AbstractAlthough highly automated vehicle fleets have committed a safe and efficient traffic flow, the next few years are likely the human‐driven vehicles (HDV) and intelligent vehicles (IV) mixed in large‐scale traffic network, where the interaction of HDVs with IVs will become more frequent. In this study, the authors investigate the traffic flow performance in a widespread of V2V environment deployed for all types of HDVs and IVs, levelled from L0 to L5. Based on this assumption, the vehicle movements and operations are collected and assembled into a V2X BSM dataset, and to any vehicle in fleet, the sequential movements and driving behaviours of the vehicle ahead can be predicted in the short term and considered as the input to cooperative adaptive cruise control (CACC) model of the controlled vehicle. The predicted motion of the vehicle ahead is applied to rolling optimization process of a given MPC framework, which calculates appropriate longitudinal operation for each car‐following vehicle and keeps optimal equilibrium state of the vehicle fleets in heterogeneous traffic flow. The experimental results showed that when the front vehicle generates random acceleration/deceleration in saturated and over saturated traffic state, in the car‐following case, the improved CACC controller maintains average car‐following distance at 20–40 m, the acceleration variation in the range [−0.1, 0.25], the gap error changed within [−10, 13], and controls the relative speed variation from −0.25 to 0.3 m/s, compared to an average headway of 20–140 m, acceleration variation in the range [−1.6, 0.65], gap error changed within [−21, 30], and a relative speed variation from −1.75 to 4.1 m/s by the traditional CACC controller. In the car‐in and car‐out cases, the vehicle controlled by the improved CACC controller had a travel displacement of 4050 m, a speed fluctuation interval of [18.7, 27.7], and an acceleration variation range of [−0.15, 0.2], while the vehicle controlled by the traditional MPC controller had a travel displacement of 3800 m, a speed fluctuation interval of [13.8,27.7] and an acceleration variation range of [−2.9, 0.6]. The improved CACC model can adapt more accurately and quickly to the maneouvre of vehicle ahead in V2V environment, which can effectively improve the capacity of heterogeneous traffic flow and the safety and comfort of driving.

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.

Energy-oriented car-following control for a front- and rear-independent-drive electric vehicle platoon

In this paper, a cooperative multi-objective platoon control (CMOPC) strategy is proposed for a front- and rear-independent-drive electric vehicle (FRIDEV) platoon. Two major measures are taken in this approach to enhance platoon economy. Firstly, a novel multi-objective nonlinear model predictive control (NMPC) system is devised by combining car-following control for connected and automated vehicle platoons and torque distribution control for FRIDEVs to exploit the energy-saving potential of an FRIDEV platoon. Secondly, the total power of all vehicles in the platoon is considered in the cost function to better reflect the overall energy consumption of the platoon. The proposed CMOPC strategy is evaluated under three typical driving cycles, i.e. UDDS, WLTC and HWFET; the ecological cooperative adaptive cruise control strategy is employed as the benchmark of evaluation. The comparative simulation results demonstrate that the proposed CMOPC not only ensures equivalent car-following performance, but also improves the platoon economy by 3.0%, 1.6% and 4.7% respectively under UDDS, WLTC and HWFET driving cycles.

Modified Cooperative Adaptive Cruise Control Strategy for Optimizing Cooperative Driving in Freeway Weaving Segments

This paper comprehensively investigates the feasibility of a modified cooperative adaptive cruise control (CACC) strategy to support car platooning in freeway weaving segments. Firstly, to improve the system response to cut-out and cut-in disturbance, the control structure of the typical original CACC system was modified and a synthesis control structure was designed that integrates the feedforward control module to the conventional feedback CACC system. Based on the modified control structure, a variable acceleration limited (VAL) control algorithm is proposed, which is specifically applied in the feedforward control model to suppress the propagation of the cut-in and cut-out disturbances in the CACC platoon. To investigate the performance of the VAL-CACC strategy, a series of comparative simulation experiments were carried out under simulated freeway weaving areas. In these tested scenarios, the occurance of frequent lane changing is considered, and the cut-out and cut-in disturbance of the CACC platoon are investigated, respectively. The obtained experimental results revealed that, compared with the original CACC system, the proposed VAL-CACC strategy can ameliorate the acceleration fluctuation amplitude, reducing it by 12.5% in the tested cut-out scenario. On the other hand, in the cut-in comparative experiments, it is found out that the VAL-CACC strategy can more significantly reduce by about 48.33% the jitter amplitude caused by the merging-in behavior of the non-platooned vehicle. These results demonstrate that the proposed synthesis VAL-CACC strategy can dramatically improve the CACC platoon’s ability to resist cut-in and cut-out disturbances.

A Safety Reinforced Cooperative Adaptive Cruise Control Strategy Accounting for Dynamic Vehicle-to-Vehicle Communication Failure.

Cooperative Adaptive Cruise Control (CACC) is an advanced technique for organizing and managing a vehicle platoon, which employs the Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I, or V2X) wireless communication to minimize the inter-vehicle distance while guaranteeing string-stability. Consequently, the conventional CACC system relies heavily on the quality of communications, which means that the regular CACC platoon is sensitive to the communication failure. Therefore, in this paper, a Safety Reinforced Cooperative Adaptive Cruise Control (SR-CACC) strategy is proposed to resist unexpected communication failure. Different from the regular CACC system, the safety enhanced platoon control system is embedded with a dual-branch control strategy. When a fatal wireless communication failure is detected and confirmed, the SR-CACC system will automatically activate the alternative sensor-based adaptive cruise control strategy. Moreover, to make the transforming process smooth, a linear smooth transition algorithm is added to the SR-CACC system. Then, to verify the performance of the proposed SR-CACC system, we conducted a simulation experiment with a heterogonous platoon constructed with eight vehicles. The experiments results reveal that, under the extremely poor communication environment, the proposed SR-CACC strategy can significantly improve the safety performance of the organized vehicle platoon.

Cooperative Adaptive Cruise Control Strategy Optimization for Electric Vehicles Based on SA-PSO With Model Predictive Control

The Cooperative Adaptive Cruise Control (CACC) is considered to be an effective method to improve traffic flow. However, the comfortability and economy need to be paid more attention to besides stability and safety. In this paper, a CACC strategy is proposed for an electric vehicle platoon to improve the economy, following performance, safety, and comfortability characteristics, based on the model predictive control (MPC) with simulated annealing-particle swarm optimization (SA-PSO) algorithm. Firstly, the braking force distribution strategy of electric vehicles is designed to improve the efficiency of regenerative braking. Secondly, based on the variable vehicle spacing with fixed time headway, a vehicle platoon following strategy is established to meet the following performance and safety. Thirdly, an MPC controller is used to control the states of the vehicles in the platoon to satisfy the performance. A multi-objective function of the MPC controller is established, including the economy, following performance, comfortability, and safety of the vehicles in the platoon. The SA-PSO algorithm effectively solves the problem of the discrete variables in the objective function. Simulations are conducted to validate the sufficient conditions of the economy, following performance, comfortability, and safety. Simulation results demonstrate that the economic efficiency of the CACC strategy with the economic index is 16.5% higher than that of the existing ACC strategy. Meanwhile, the other characteristics can also meet the control requirement.

Combined Cooperative Adaptive Cruise Control using Collective Initial Excitation based Distributed Parameter Estimator

In cooperative adaptive cruise control (CACC), autonomous vehicles are grouped into a string of platoon and, the main objective is to automatically adapt their speed using on-board sensors and communication with the preceding vehicle to maintain a desired inter-vehicle distance. Cruise control is achieved in the presence of parametric uncertainty in the vehicle dynamics using principles of adaptive control. This work proposes a novel combined CACC strategy for an uncertain homogeneous platoon with guaranteed parameter convergence and asymptotic string stability. A novel distributed consensus-based parameter estimator is proposed in conjunction with a model reference adaptive control (MRAC) algorithm using a direct control-gain update law. The algorithm ensures exponential parameter estimation error convergence to zero as well as asymptotic convergence of tracking-error to zero. Conventional CACC protocols require a condition of persistence of excitation (PE) for parameter convergence, which is required for better transient performance in converging to a string stable configuration. The PE condition is highly restrictive in the context of cruise control since velocity profiles which are demanded in the platoon model do not typically satisfy the PE condition. In contrast, the proposed scheme can ensure parameter convergence under a significantly milder condition, coined as collective initial excitation (C-IE). The C-IE condition is an extension of the concept of initial excitation (IE), which is recently proposed in the context of adaptive control of single agent system. Unlike IE, the C-IE condition caters to distributed estimation in the context of multi-agent systems. As far as the authors are aware, this is the first work on CACC framework, which ensures exponential convergence of parameter estimation error of each vehicle under the mild condition of C-IE, which further leads to asymptotic convergence of the entire vehicle platoon to a string stable configuration. Simulation study dictates that the proposed CACC architecture outperforms the existing CACC algorithms in terms of tracking and estimation performance.

Compensation of Communication Delays in a Cooperative ACC System

Cooperative adaptive cruise control (CACC) employs intervehicle wireless communications to safely drive at short intervehicle distances, which improves road throughput. The underlying technical requirement to achieve this benefit is formulated by the notion of string stability, requiring the attenuation of the effects of disturbances in upstream direction. The wireless communication delay, however, significantly compromises string stability. In order to compensate for time delays and thus reduce the minimum string-stable time gap, a Smith predictor can be applied. For application of a Smith predictor, the time delay needs to be in a series connection with the plant to be controlled, which is realized by introducing a master-slave architecture for CACC. As a result, information exchange appears to become bidirectional, while the control scheme still follows the one-vehicle look-ahead strategy. Feasibility of both the master-slave CACC strategy and the Smith predictor is explicitly analyzed. With the proposed control scheme, the minimum string-stable time gap can be significantly decreased, even considering communication delay uncertainty. The results are validated using simulations with a platoon of CACC-equipped vehicles.

An Adaptive Switched Control Approach to Heterogeneous Platooning With Intervehicle Communication Losses

The advances in distributed intervehicle communication networks have stimulated a fruitful line of research in cooperative adaptive cruise control (CACC). In CACC, individual vehicles, grouped into platoons, must automatically adjust their own speed using on-board sensors and communication with the preceding vehicle so as to maintain a safe intervehicle distance. However, a crucial limitation of the state of the art of this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics and perfect engine performance (homogeneous platoon), and possibly an ideal communication channel. This paper proposes a novel CACC strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability even for uncertain heterogeneous platoons. Furthermore, in order to handle the inevitable communication losses, we formulate an extended average dwell-time framework and design an adaptive switched control strategy, which activates an augmented CACC or an augmented adaptive cruise control strategy depending on communication reliability. Stability is proven analytically and simulations are conducted to validate the theoretical analysis.

An improved car-following model with multiple preceding cars’ velocity fluctuation feedback

In order to explore and evaluate the effects of velocity variation trend of multiple preceding cars used in the Cooperative Adaptive Cruise Control (CACC) strategy on the dynamic characteristic, fuel economy and emission of the corresponding traffic flow, we conduct a study as follows: firstly, with the real-time car-following (CF) data, the close relationship between multiple preceding cars’ velocity fluctuation feedback and the host car’s behaviors is explored, the evaluation results clearly show that multiple preceding cars’ velocity fluctuation with different time window-width are highly correlated to the host car’s acceleration/deceleration. Then, a microscopic traffic flow model is proposed to evaluate the effects of multiple preceding cars’ velocity fluctuation feedback in the CACC strategy on the traffic flow evolution process. Finally, numerical simulations on fuel economy and exhaust emission of the traffic flow are also implemented by utilizing VT-micro model. Simulation results prove that considering multiple preceding cars’ velocity fluctuation feedback in the control strategy of the CACC system can improve roadway traffic mobility, fuel economy and exhaust emission performance.

The effects of velocity difference changes with memory on the dynamics characteristics and fuel economy of traffic flow

Abstract To evaluate the effects of velocity difference changes with memory in the intelligent transportation environment on the dynamics and fuel consumptions of traffic flow, we first investigate the linkage between velocity difference changes with memory and car-following behaviors with the measured data in cities, and then propose an improved cooperative car-following model considering multiple velocity difference changes with memory in the cooperative adaptive cruise control strategy, finally carry out several numerical simulations under the periodic boundary condition and at signalized intersections to explore how velocity difference changes with memory affect car’s velocity, velocity fluctuation, acceleration and fuel consumptions in the intelligent transportation environment. The results show that velocity difference changes with memory have obvious effects on car-following behaviors, that the improved cooperative car-following model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion, that the stability and fuel economy of traffic flow simulated by the improved car-following model with velocity difference changes with memory is obviously superior to those without velocity difference changes, and that taking velocity difference changes with memory into account in designing the advanced adaptive cruise control strategy can significantly improve the stability and fuel economy of traffic flow.

Fuel consumptions and exhaust emissions induced by cooperative adaptive cruise control strategies

Many cooperative adaptive cruise control strategies have been presented to improve traffic efficiency as well as road traffic safety, but scholars have rarely explored the impacts of these strategies on cars' fuel consumptions and exhaust emissions. In this paper, we respectively select two-velocity difference model, multiple velocity difference model and the car-following model considering multiple preceding cars' accelerations to investigate each car's fuel consumptions, carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides ( NOX) emissions and carry out comparative analysis. The comparisons of fuel consumptions and exhaust emissions in three different cruise control strategies show that cooperative cars simulated by the car-following model considering multiple preceding cars' accelerations can run with the minimal fuel consumptions, CO, HC and NOXemissions, thus, taking the car-following model considering multiple preceding cars' accelerations as the cooperative adaptive cruise control strategy can significantly improve cars' fuel efficiency and exhaust emissions.

The effects of vehicular gap changes with memory on traffic flow in cooperative adaptive cruise control strategy

To evaluate the impacts of new influence factor in cooperative adaptive cruise control strategy on the dynamic characteristics of traffic flow, an improved cooperative car-following model considering multiple vehicular gap changes with memory is developed to study the influences of multiple vehicular gap changes with memory on each car’s speed, acceleration and relative distance. Some numerical simulations are carried out and the results show that considering multiple vehicular gap changes with memory in designing the cooperative adaptive cruise control strategy can improve the stability of traffic flow and reduce the accidental probability.