Extending the adaptive time gap car-following model to enhance local and string stability for adaptive cruise control systems

2021 ◽  
pp. 1-21
Author(s):  
Parthib Khound ◽  
Peter Will ◽  
Antoine Tordeux ◽  
Frank Gronwald
Keyword(s):  
Control Systems ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
String Stability ◽  
Car Following ◽  
Car Following Model ◽  
Adaptive Time ◽  
Time Gap

Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems

2017 ◽  
Vol 2622 (1) ◽  
pp. 84-95 ◽  
Author(s):  
Mizanur Rahman ◽  
Mashrur Chowdhury ◽  
Kakan Dey ◽  
M. Rafiul Islam ◽  
Taufiquar Khan
Keyword(s):  
Vehicle Dynamics ◽  
Adaptive Cruise Control ◽  
User Acceptance ◽  
Evaluation Framework ◽  
Cruise Control ◽  
String Stability ◽  
Maximum Acceleration ◽  
Car Following ◽  
Cooperative Adaptive Cruise Control ◽  
Behavior Models

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, ( a) the optimum velocity model (OVM) and ( b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.

Intelligent Cruise Control Systems and Traffic Flow Behavior

1999 ◽  
Author(s):  
Darbha Swaroop ◽  
K. R. Rajagopal
Keyword(s):  
Control Systems ◽  
Traffic Flow ◽  
Adaptive Cruise Control ◽  
Flow Behavior ◽  
Flow Stability ◽  
Traffic Density ◽  
Cruise Control ◽  
String Stability ◽  
Control Laws ◽  
Traffic Flow Stability

Abstract In analogy to the flow of fluids, it is expected that the aggregate density and the velocity of vehicles in a section of a freeway adequately describe the traffic flow dynamics. The conservation of mass equation together with the aggregation of the vehicle following dynamics of controlled vehicles describes the evolution of the traffic density and the aggregate speed of a traffic flow. There are two kinds of stability associated with traffic flow problems — string stability (or car-following stability) and traffic flow stability. We make a clear distinction between traffic flow stability and string stability, and such a distinction has not been recognized in the literature, thus far. String stability is stability with respect to intervehicular spacing; intuitively, it ensures the knowledge of the position and velocity of every vehicle in the traffic, within reasonable bounds of error, from the knowledge of the position and velocity of a vehicle in the traffic. String stability is analyzed without adding vehicles to or removing vehicles from the traffic. On the other hand, traffic flow stability deals with the evolution of traffic velocity and density in response to the addition and/or removal of vehicles from the flow. Traffic flow stability can be guaranteed only if the velocity and density solutions of the coupled set of equations is stable, i.e., only if stability with respect to automatic vehicle following and stability with respect to density evolution is guaranteed. Therefore, the flow stability and critical capacity of any section of a highway is dependent not only on the vehicle following control laws and the information used in their synthesis, but also on the spacing policy employed by the control system. Such a dependence has practical consequences in the choice of a spacing policy for adaptive cruise control laws and on the stability of the traffic flow consisting of vehicles equipped with adaptive cruise control features on the existing and future highways. This critical dependence is the subject of investigation in this paper. This problem is analyzed in two steps: The first step is to understand the effect of spacing policy employed by the Intelligent Cruise Control (ICC) systems on traffic flow stability. The second step is to understand how the dynamics of ICC system affects traffic flow stability. Using such an analysis, it is shown that cruise control systems that employ a constant time headway policy lead to unacceptable characteristics for the traffic flows.

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

2015 ◽  
Vol 29 (14) ◽  
pp. 1550084 ◽  
Author(s):  
Shaowei Yu ◽  
Zhongke Shi
Keyword(s):  
Adaptive Cruise Control ◽  
Control Strategies ◽  
Exhaust Emissions ◽  
Cruise Control ◽  
Difference Model ◽  
Velocity Difference ◽  
Car Following ◽  
Road Traffic Safety ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model

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 ( NO X ) 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 NO X emissions, 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.

scholarly journals Realistic Car-Following Models for Microscopic Simulation of Adaptive and Cooperative Adaptive Cruise Control Vehicles

2017 ◽  
Vol 2623 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Lin Xiao ◽  
Meng Wang ◽  
Bart van Arem
Keyword(s):  
Adaptive Cruise Control ◽  
Model Performance ◽  
Cruise Control ◽  
Vehicle Collisions ◽  
Car Following ◽  
Cooperative Adaptive Cruise Control ◽  
Proposed Model ◽  
Wide Range ◽  
Car Following Model ◽  
Vehicle Response

Adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) are important technologies for the achievement of vehicle automation, and their effect on traffic systems generally is evaluated with microscopic traffic simulations. A successful simulation requires realistic vehicle behavior and minimal vehicle collisions. However, most existing ACC-CACC simulation studies used simplified models that were not based on real vehicle response. The studies rarely addressed collision avoidance in the simulation. The study presented in this paper developed a realistic and collision-free car-following model for ACC-CACC vehicles. A multiregime model combining a realistic ACC-CACC system with driver intervention for vehicle longitudinal motions is proposed. This model assumes that a human driver resumes vehicle control either according to his or her assessment or after a collision warning asks the driver to take over. The proposed model was tested in a wide range of scenarios to explore model performance and collision possibilities. The testing scenarios included three regular scenarios of stop-and-go, approaching, and cut-out maneuvers, as well as two extreme safety-concerned maneuvers of hard brake and cut-in. The simulation results show that the proposed model is collision free in the full-speed-range operation with leader accelerations within −1 to 1 m/s2 and in approaching and cut-out scenarios. Those results indicate that the proposed ACC-CACC car-following model can produce realistic vehicle response without causing vehicle collisions in regular scenarios for vehicle string operations.

An adaptive time gap car-following model

2010 ◽  
Vol 44 (8-9) ◽  
pp. 1115-1131 ◽  
Author(s):  
Antoine Tordeux ◽  
Sylvain Lassarre ◽  
Michel Roussignol
Keyword(s):  
Car Following ◽  
Car Following Model ◽  
Adaptive Time ◽  
Time Gap

Robust optimal control and identification of adaptive cruise control systems in the presence of time delay and parameter uncertainties

2020 ◽  
Vol 26 (17-18) ◽  
pp. 1590-1601 ◽  
Author(s):  
Hossein Chehardoli
Keyword(s):  
Optimal Control ◽  
Cost Function ◽  
Control Systems ◽  
Adaptive Cruise Control ◽  
System Modeling ◽  
Internal Stability ◽  
Cruise Control ◽  
Parameter Uncertainties ◽  
String Stability ◽  
Robust Optimal Control

This article deals with the robust optimal control and identification of uncertain adaptive cruise control systems. Both measurement delay and engine’s lag are investigated in system modeling and control design. The control structure consists of two steps. In the first step, by using a new approach, the uncertain parameters of longitudinal dynamics of each vehicle is identified. Afterwards, at the second step, by using the relative position and velocity measurement with respect to ahead vehicle, a robust control against size changing of platoon is presented to assure the internal stability, string stability, and safety of heterogeneous vehicular platoons. A cost function involving important metrics internal stability, maximum overshoot, and settling time is introduced, and the particle swarm optimization algorithm is used to find the optimal values of control parameters minimizing the cost function. It will be shown that the proposed robust controller guarantees the internal stability, string stability, and safety of adaptive cruise control systems in the presence of measurement and parasitic delays. Several simulation results are provided to show the effectiveness of the proposed approaches.

scholarly journals Control Design, Stability Analysis, and Traffic Flow Implications for Cooperative Adaptive Cruise Control Systems with Compensation of Communication Delay

2020 ◽  
Vol 2674 (8) ◽  
pp. 638-652
Author(s):  
Yu Zhang ◽  
Yu Bai ◽  
Jia Hu ◽  
Meng Wang
Keyword(s):  
Traffic Flow ◽  
Local Stability ◽  
Adaptive Cruise Control ◽  
Control Design ◽  
Communication Delay ◽  
Cruise Control ◽  
String Stability ◽  
Cooperative Adaptive Cruise Control ◽  
Time Gap ◽  
Traffic Flow Stability

Communication delay is detrimental to the performance of cooperative adaptive cruise control (CACC) systems. In this paper, we incorporate communication delay explicitly into control design and propose a delay-compensating CACC. In this new CACC system, the semi-constant time gap (Semi-CTG) policy, which is modified on the basis of the widely-used CTG policy, is employed by a linear feedback control law to regulate the spacing error. The semi-CTG policy uses historical information of the predecessor instead of its current information. By doing so, communication delay is fully compensated, which leads to better stability performance. Three stability properties—local stability, string stability, and traffic flow stability—are analyzed. The local stability and string stability of the proposed CACC system are guaranteed with the desired time gap as small as the communication delay. Both theoretical analysis and simulation results show that the delay-compensating CACC has better string stability and traffic flow stability than the widely-used CACC system. Furthermore, the proposed CACC system also shows the potential for improving traffic throughput and fuel efficiency. Robustness of the proposed system against uncertainties of sensor delay and vehicle dynamics is also verified with simulation.

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