Cooperative Adaptive Cruise Control Model Research Articles

Multi-objective optimization for connected and automated truck platoon control with improved CACC model

Connected and Automated Truck Platoon (CATP) refers to a group of trucks traveling closely together with minimal spacing to improve fuel economy and safety. However, challenges arise from instability due to internal platoon factors and external traffic disturbances. This research presents an improved Cooperative Adaptive Cruise Control (CACC) model tailored for CATP to address these challenges. The model is designed to enhance safety, fuel efficiency, and traffic efficacy. The improvements of the proposed model are in two aspects: the optimizing of the time headway strategy and the dynamic parameter adjustments of controller based on multi-objectives. The Dynamic Safety Requirement Time Headway (DSRTH) strategy facilitates the timely detection of the accelerations of the leading vehicles within the platoon, enabling quick driving responses. Additionally, Model Predictive Control (MPC) enables dynamic calibration of Proportional-Derivative (PD) control parameters and issuance of velocity commands. Meanwhile, the integration of a second-order time-delay response model has been implemented to adapt to dynamic changes in commands. A transfer function has been established, and stability has been proven. To evaluate the model performance, simulation analysis was performed using real vehicle trajectories as the CATP following vehicles. The results indicate that the DSRTH strategy outperforms both the Constant Time Headway (CTH) and Variable Time Headway (VTH) strategies, allowing rear vehicles to reach the speed trough earlier, with response speeds improved by 3.1 % and 1.5 %, respectively. Compared to the Intelligent Driver Model (IDM) and CACC models, the improved CACC model achieves a steady state of constant acceleration sooner, with recovery times reduced by 17.7 % and 3.2 %. Additionally, compared to the IDM model, the improved CACC model can save 3.23 % in fuel consumption. Furthermore, sensitivity analysis indicates that as the CATP proportion and platoon size increase, there is a positive impact on traffic flow. However, when the platoon size exceeds 5 vehicles, it shows a negative impact on the stability of other vehicles in the traffic flow besides those in the CATP.

Effects of uncertain anomalous information on traffic flow of automated vehicles with V2V communication

Automated vehicles (AVs) equipped with vehicle-to-vehicle (V2V) communication can operate by sensing real-time status information through onboard sensors and wireless connections. Nevertheless, under the influence of multifarious random factors in real traffic, this critical information that support the normal movement of such vehicles may be anomalous, raising concerns on their mobility and traffic security. Due to the lack of appropriate analytical model, previous studies have not comprehensively uncovered the effects of uncertain anomalous information on traffic flow of AVs with V2V communication. Therefore, this study aims to bridge this critical gap. Firstly, by introducing a probabilistic parameter (i.e., information anomaly probability), we propose a general model that integrates the normal and compromised models, thereby capturing the longitudinal dynamics of AVs featuring V2V communication in the presence of uncertain anomalous information. To enable the detailed theoretical and experimental analyses, we specify it through the cooperative adaptive cruise control model calibrated with real-car data. Subsequently, we define the concept of pseudo string stability and parameterize the stability condition based on the characteristic equation method, so as to demonstrate the relationship between traffic flow stability and the parameters and probability of information anomaly. Finally, we refine the proposed probabilistic model and conduct extensive numerical experiments. The findings show that uncertain anomalous information could result in sudden or even frequent acceleration and deceleration of AVs, causing traffic oscillation, reduced traffic efficiency, and even collision accidents. In particular, the greater the information anomaly probability, the larger the disturbances experienced by traffic flow. Meanwhile, at the same level of anomaly, the combined impacts of various anomalous information could lead to more severe consequences than the singular impact of any individual anomalous information. Furthermore, the duration of anomalous information directly affects the time it takes for traffic flow to return to normal.

Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs.

Risk-informed longitudinal control in autonomous vehicles: A safety potential field modeling approach

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency.

Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments

The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.

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.

Car-Following Model Optimization and Simulation Based on Cooperative Adaptive Cruise Control

This study aims to improve the desired distance adaptability of the cooperative adaptive cruise control (CACC) during car-following. In this study, the characteristics of the desired distance in different traffic flow states were analyzed. The general functional form of the desired distance in the car-following process was formulated. Then, a car-following platoon was constructed to compare the car-following effect of the platoon under different conditions, using the following speed and the lead vehicle disturbance, as the observed variable and the simulation condition, respectively. The car-following effect of the platoon under different parameters was also compared, based on the improved CACC model. The results show that the improved CACC model exhibited more advantages in car-following efficiency, it can better describe the state of the car-following queue under different traffic flow parameters and car-following behavior conditions, it has a strong anti-interference ability for the fluctuation of the car-following queue and is conducive to further improving the intelligent operation of car-following queue.

Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag

Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics.

Investigating the Longitudinal Impact of Cooperative Adaptive Cruise Control Vehicle Degradation Under Communication Interruption

Cooperative adaptive cruise control (CACC), which is recognized as an extension of adaptive cruise control (ACC) through the addition of vehicle-to-vehicle (V2V) communication, is promoted for application in the near future. Nevertheless, V2V communication is unreliable due to the communication interruption caused by cyberattacks and information congestion. When communication interruption occurs, a CACC vehicle cannot receive the travel information of the preceding vehicle and automatically degrades to an ACC vehicle, leading to the instability of the CACC fleet. This research aims to investigate the longitudinal impact of CACC vehicle degradation under communication interruption. The realistic CACC model and ACC model are used for constructing simulation experiments with a fleet of 10 CACC vehicles. By simulating multiple driving, degradation, and communication-interruption scenarios, we investigate the impact of CACC degradation on fleet stability, energy consumption, and exhaust emissions. Finally, we conduct sensitivity analysis on the key factors of driving models and simulation scenarios. The result shows that CACC vehicle degradation, especially the successive degradation of adjacent vehicles under the acceleration scenario, would reduce average travel speed and increase energy consumption and exhaust emissions. The findings highlight some key scenarios for actively monitoring communication interruption and provide some prospective actions to reduce the impact of CACC degradation.

Safety Analysis of a Modified Cooperative Adaptive Cruise Control Algorithm Accounting for Communication Delay

Cooperative adaptive cruise control (CACC) is a promising technology to improve traffic efficiency and enhance road safety. In this paper, a modified CACC control model considering the communication time delay is proposed, which is used to investigate the longitudinal safety impacts of the communication time delay to the CACC platoon. Then, the communication time delay model is integrated into the CACC model to simulate the realistic information transfer process in the CACC platoon. Then a microscopic CACC platoon simulation is designed and conducted to verify the feasibility and reliability of the modified CACC control algorithm. The obtained results reveal that the modified CACC control algorithm can not only reduce about 96.6% of inter-vehicle spacing error, but also enhance the vehicles’ ability to sense the upstream traffic changes. Furthermore, to quantitatively analyze the longitudinal safety influence of the time delay caused by representative communication systems, sensitivity analysis experiments of headway time were designed and conducted. In the sensitivity analysis, the time exposed time-to-collision (TET) and the time-integrated time-to-collision (TIT) were introduced as the key performance indicators (KPIs) to quantify the rear-end collision risks. Sensitivity analysis results demonstrate that the performance of the CACC platoon is strictly related to the applied wireless communication style. Furthermore, the CACC system supported by the 5th generation (5G) communication system shows great advantages in narrowing the minimal headway time gap and reducing the rear-end collision risks.

Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles

With the development of technology, platoons of intelligent and connected vehicles (ICVs) will join regular vehicles on roads, and the characteristics of the traffic flow will change accordingly. In this paper, the traffic flow configurations and the spatial distributions of various types of vehicles are analyzed, when the mixed traffic flow is in equilibrium. To model the mixed traffic flow with platoons of ICVs, the intelligent driver model and the PATH laboratory-calibrated cooperative adaptive cruise control model are introduced for capturing the car-following behavior in this study. Then, analytical methods for the stability and the fundamental diagram models of mixed traffic flow are developed. The results of the sensitivity analysis demonstrated that ICVs can improve the stability of the mixed traffic flow under a critical speed. However, if this critical speed is exceeded, an increase in the number of ICVs may degrade the stability of the mixed traffic flow. Moreover, this critical speed decreases as the maximum platoon size increases. In addition to stability, the results suggest that ICVs can effectively improve traffic capacity. Moreover, the study results demonstrate that a decrease in the desired time headway and an increase in the maximum platoon size are advantageous for the efficiency of the mixed flow.

Anomaly Detection for Cooperative Adaptive Cruise Control in Autonomous Vehicles Using Statistical Learning and Kinematic Model

This paper focuses on Cooperative Adaptive Cruise Control (CACC) in autonomous vehicles. In CACC, vehicles regulate their speed according to a preceding “leader” vehicle in the lane, forming a platoon. In a benign environment, CACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, CACC is vulnerable to various security threats. In this paper, we consider one of the critical threats, where the platoon leader is compromised, and forges acceleration information sent to platoon members. Such attack would lead to traffic instability and potential collisions. First, we propose information sharing in CACC model to allow vehicles and fixed infrastructure to sense and share information about platoon leaders, hence improves the reliability and supports the detection of anomalous behavior. Then, we propose a real-time anomaly detection mechanism that combines statistical learning with the physics laws of kinematics. Specifically, we propose Generalized Extreme Studentized Deviate with Sliding Chunks (GESD-SC) approach, which is applied at each vehicle in the platoon to detect anomalies in real-time based on the vehicle's own speeding decisions. Kinematic model is also utilized to detect unexpected deviations using the leader's information, communicated directly and observed by the leader's neighboring vehicle(s) and/or supporting infrastructure. Combining kinematic model with GESD-SC has shown to be effective in detecting falsification attacks in CACC. Furthermore, we analyze the time performance, and show that the proposed technique outperforms existing method in detection accuracy and processing time.

A dynamic merge assistance method based on the concept of instantaneous virtual trajectory for vehicle-to-infrastructure connected vehicles

Lane-changing activities near freeway merging, diverging, and weaving sections are one of the major factors leading to recurrent bottleneck congestion. In recent years, microscopic dynamic merging assistance (DMA) methods are found efficient to improve mobility and safety in merging areas. The article proposes a solution, the connected vehicle (CV) vehicle-to-infrastructure (V2I)-based dynamic merge assistance (DMA) method, analyzing vehicle trajectory data collected at CV roadside units (RSU) and implements a “vehicle-gap” pairing process for vehicle-gap synchronized control. The vehicle-gap pairing is determined by dynamically predicting their merging potential per the relationship between the “instantaneous virtual trajectories” (IVTs) of mainline and onramp vehicles. The IVTs are generated according to the prevailing speed profiles on both ramp and mainline through lane. Other than those automated control-based methods, this article focuses on providing a realistic speed instruction to human drivers through CV communication. A Cooperative Adaptive Cruise Control model is adopted to calculate such speed instruction. The paired onramp/mainline vehicles who follow the speed instruction can form a putative platoon so that their speed and location can be synchronized all through the merging area to ensure the gap-opening and catching-up. The proposed method is evaluated within a simulation network based on the field data collected on Interstate 35 at Austin, TX. The proposed method is tested in a microscopic traffic simulation environment using VISSIM external driver model Application Programing Interface. The simulation results indicated significant reduced average travel time at merge sections during congestion and increased time-to-collision during merging compared with other existing microscopic merge control models.

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.

Time-delay feedback cooperative adaptive cruise control of connected vehicles by heterogeneous channel transmission

In this paper, we consider the cooperative adaptive cruise control problem of connected autonomous vehicles networked by heterogeneous wireless channel transmission. The cooperative adaptive cruise control model with variable input delays is established to describe the varying time-delays induced from vehicular actuators and heterogeneous channel transmission. Then a set of decentralized time-delay feedback cooperative adaptive cruise control controllers is computed in such way that each vehicle evaluates its own adaptive cruise control strategy using only neighborhood information. In order to establish string stability of the connected vehicle platoon with the decentralized controllers, the sufficient conditions are obtained in the form of linear matrix inequalities. The scenarios, consisting of four different cars with three heterogeneous wireless channels, are used to demonstrate the effectiveness of the presented method.

Networked Model for Cooperative Adaptive Cruise Control

This paper proposes a cooperative adaptive cruise control model, adding a communication network structure to an existing model that has been shown to capture real commercial adaptive cruise control vehicle behavior. The proposed model is interesting because it only requires minimal information sharing, facilitating the creation of platoons comprised of vehicles from different manufacturers. We prove the stability of the model and discuss string stability. Algorithms for estimating the velocity of the vehicles locally and for estimating the velocities of all the vehicles in the platoon are presented. We simulate vehicle platoon control with the lead vehicle following an experimentally collected trajectory, showing that adding communication can cause a string unstable platoon to become stable.

Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks

Cooperative Adaptive Cruise Control (CACC) systems have the potential to increase roadway capacity and mitigate traffic congestion thanks to the short following distance enabled by inter-vehicle communication. However, due to limitations in acceleration and deceleration capabilities of CACC systems, deactivation and switch to ACC or human-driven mode will take place when conditions are outside the operational design domain. Given the lack of elaborate models on this interaction, existing CACC traffic flow models have not yet been able to reproduce realistic CACC vehicle behaviour and pay little attention to the influence of system deactivation on traffic flow at bottlenecks. This study aims to gain insights into the influence of CACC on highway operations at merging bottlenecks by using a realistic CACC model that captures driver-system interactions and string length limits. We conduct systematic traffic simulations for various CACC market penetration rates (MPR) to derive free-flow capacity and queue discharge rate of the merging section and compare these to the capacity of a homogeneous pipeline section. The results show that an increased CACC MPR can indeed increase the roadway capacity. However, the resulting capacity in the merging bottleneck is much lower than the pipeline capacity and capacity drop persists in bottleneck scenarios at all CACC MPR levels. It is also found that CACC increases flow heterogeneity due to the switch among different operation modes. A microscopic investigation of the CACC operational mode and trajectories reveals a close relation between CACC deactivation, traffic congestion and flow heterogeneity.

Hybrid traffic flow model for intelligent vehicles exiting to off-ramp

With the rapid development of vehicular technology, hi-tech manufacturing facilities are equipped in intelligent vehicles to improve road capacity and traffic safety. However, freeway diverge segment has significant influence on current traffic flow, and could affect the heterogeneous traffic flow consisting of manual and intelligent vehicles. The primary objective of this study is to evaluate how intelligent vehicles affect traffic flow at an off-ramp bottleneck.In order to depict the car-following dynamics of manual vehicles, the modified comfortable model, one of the most classic cellular automata models, is employed to distinguish intelligent vehicles. In this paper, intelligent vehicles consist of adaptive cruise control (ACC) vehicles cooperative adaptive cruise control (CACC) vehicles. The ACC and CACC model are proposed by partners for advanced transportation technology (PATH), which are validated by real experimental data. Besides, vehicles equipped with CACC will degrade ACC vehicle if the leading vehicle is driven manually. From the perspective of vehicle's lateral movement, two novel lane-changing models, including the discretionary lane-change (DLC) model and mandatory lane-change (MLC) model, are developed to model the future behaviors of intelligent vehicles. A risk factor λ is introduced into the DLC model to distinguish vehicles from conventional ones. Based on environment perception technology, a five-step MLC decision-making model is designed specifically for intelligent vehicles exiting to off-ramp. It is comprised of environment perception, safe gap computation, measured gap ranking, measured gap classification and lane-changing gap selection. Based on the proposed hybrid traffic flow model, numerical simulations are conducted to study the influences of intelligent vehicles on the traffic flow near an off-ramp. Apart from the market penetration of intelligent vehicles, parameters considered in this paper include the demands of mainlines and off-ramp, range of environment perception, length of lane-changing area, and level of lane-changing risk.Analytical studies and simulation results are as follows. 1) The integration of car-following model and lane-changing model for the off-ramp system enables vehicles to have reasonable dynamic characteristics. 2) The capacity ascends to the peak after an initial decrease as CACC vehicle penetration increases. The maximum capacity obtained in 100% CACC vehicle scenario is improved by over 50%, compared with that in 50% CACC penetration scenario. 3) Enlarging the ranges of environment perception and lane-changing areas, and enhancing the lane-changing risk can significantly dissipate congestion upstream of the off-ramp and improve the efficiency of mainlines. However, they have little influence on traffic flow at off-ramp. 4) The worst performance of the system occurs in the scenario of 50% CACC penetration, where deterioration caused by degraded ACC vehicles suggests that enough patience and public confidence should be paid for the development of intelligent vehicles.

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.

Stability analysis and fundamental diagram of heterogeneous traffic flow mixed with cooperative adaptive cruise control vehicles

This paper is aimed at building a framework for string stability analysis of traffic flow mixed with different cooperative adaptive cruise control (CACC) market penetration rates. In addition to the string stability, the fundamental diagram of the mixed flow is also taken into consideration for evaluating the effect of CACC vehicles on capacity. In order to describe the car-following dynamics of real CACC vehicles, the CACC model proposed by PATH is employed, which is validated by real experimental data. The intelligent driver model (IDM) is used as a surrogate car-following model for traditional manual driven vehicles. Based on the guidelines proposed by Ward[Ward J A 2009 Ph. D. Dissertation (Bristol:University of Bristol)], a framework is developed for the analytical investigation of heterogeneous traffic flow string stability. The framework presented considers the instability condition of traffic flow as a linear function of CACC market penetration rate. Following the framework, the string stabilities of the mixed traffic flow under different CACC market penetration rates and equilibrium velocities are analyzed. For fundamental diagram of the heterogeneous traffic flow, the equilibrium velocity-spacing functions of manual vehicles and CACC vehicles are obtained respectively based on car-following model. Then, the fundamental diagram of the density-velocity relationship of the heterogeneous traffic flow is derived based on the definition of traffic flow density. In addition, the theoretical fundamental diagram is plotted to show the property of traffic throughput. The numerical simulations are also carried out in order to investigate the effect of CACC vehicle on the characteristics of fundamental diagram. Besides, sensitivity analyses on CACC desired time gap are conducted for both string stability and fundamental diagram. Analytical studies and simulation results are as follows. 1) The heterogeneous traffic flow is stable for different equilibrium velocities and CACC market penetration rates, if manual driven vehicles are stable. Otherwise, the instability of traditional traffic flow is improved gradually with the increase of the CACC market penetration rate. Additionally, the stability will become better when equilibrium velocity is away from the velocity range of 9.6-18.6 m/s. 2) Because CACC vehicles can travel at free-flow speed in a relatively small headway, CACC vehicles can improve the capacity of heterogeneous traffic flow. 3) The results of sensitivity analysis indicate that with the increase of the CACC desired time gap, the stable region of heterogeneous traffic flow increases. However, the capacity of the fundamental diagram drops. Therefore, the value of the desired time gap should be determined with considering the effects of the two aspects on the heterogeneous traffic flow. It is noted that the CACC model used in this paper is based on the current state-of-the-art real CACC vehicle experiments. In the future, more experimental observations will yield new CACC models. However, the framework presented in this paper can still be used for the analytical investigation of string stability of the heterogeneous traffic flow at that time.