MVCM Car-Following Model for Connected Vehicles and Simulation-Based Traffic Analysis in Mixed Traffic Flow

Wang, Shuyi; Yu, Bin; Wu, Miyi

doi:10.1109/tits.2021.3052818

Cited by 31 publications

(9 citation statements)

References 29 publications

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“…Figure 3a is the cognitive presentation of AVs basics. For a more elaborate discussion on the automation level of AVs, readers are referred to 35 . The mass production of tools relevant to AVs is approaching thanks to rapid advancements in AVs technologies, particularly the recent advancements in LiDAR, GPUs, and learning control strategies 33 .…”

Section: Avs Fundamentalsmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Section: Avs Fundamentalsmentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…cu are determined by (7). If the l -th vehicle in sub-platoon #i is HDV, F i l , G i l in A i c are determined by (4), while b i l in B i cu is always zero matrix.…”

Section: Mixed Vehicle Platoon Dynamicsmentioning

confidence: 99%

“…Therefore, there will be a mixed traffic system in which HDVs and CAVs coexist for a long time to come [6]. The complex traffic environment has brought great challenges to the modeling and control of mixed vehicle platoons, which attract extensive attention of scholars at home and abroad [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Cooperative predictive control for arbitrarily mixed vehicle platoons with guaranteed global optimality

Zhan

Hua

Zhang

2023

IET Intelligent Trans Sys

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This paper studies the cooperative control problem of a mixed vehicle platoon, which is composed of connected autonomous vehicles (CAVs) and human‐driven vehicles (HDVs) in an arbitrary order. An alternating direction method of multipliers (ADMM) based distributed model predictive control (DMPC) algorithm is proposed for CAVs to lead the mixed vehicle platoon travelling in a string with anticipated inter‐vehicle spacing and a desired velocity. First, the mixed vehicle platoon is divided into multiple interrelated sub‐platoons with any two adjacent sub‐platoons having a common CAV, and then a generic model is constructed for each sub‐platoon based on the intelligent driver model for HDV and the kinematic model for CAV, respectively. Second, a local MPC controller is designed for each sub‐platoon to optimize the control inputs of CAVs with the objective of minimizing the position and velocity errors of all vehicles in the sub‐platoon, and then the ADMM is utilized to obtain the global optimal solution among local MPC controllers of all sub‐platoons. Finally, numerical simulations and experiments are carried out to verify the effectiveness of the proposed DMPC algorithm, the results of which reveal that it can reduce the computation cost significantly and ensure the control performance comparable to the centralized MPC algorithm.

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“…Free acceleration exponent, δ n , the desired time gap, T n , jam distance, s 0 n , maximum acceleration, a ¯ n , desired deceleration, b ¯ n , and desired velocity, v 0 n , are parameters for vehicle n . The improved CV models, which consider the impacts of driver compliance or communication topology between vehicles ( 32–35 ), are not included in this paper because our focus is on the impact of CACC vehicles on traffic stability. Future studies could consider these improvements and use real-world CV data to calibrate model parameters to simulate more realistic cases.…”

Section: Model Formulationsmentioning

confidence: 99%

Impact of Cooperative Adaptive Cruise Control on Traffic Stability

Hung

Zhang

2022

Transportation Research Record: Journal of the Transportation R

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Cooperative adaptive cruise control (CACC) is one of the popular connected and automated vehicle (CAV) applications for cooperative driving automation with combined connectivity and automation technologies to improve string stability. This study aimed to derive the string stability conditions of a CACC controller and analyze the impacts of CACC on string stability for both a fleet of homogeneous CAVs and for heterogeneous traffic with human-driven vehicles (HDVs), connected vehicles (CVs) with connectivity technologies only, and autonomous vehicles (AVs) with automation technologies only. We mathematically analyzed the impact of CACC on string stability for both homogeneous and heterogeneous traffic flow. We adopted parameters from literature for HDVs, CVs, and AVs for the heterogeneous traffic case. We found there was a minimum constant time headway required for each parameter design to ensure stability in homogeneous CACC traffic. In addition, the constant time headway and the length of control time interval had positive correlation with stability, but the control parameter had a negative correlation with stability. The numerical analysis also showed that CACC vehicles could maintain string stability better than CVs and AVs under low HDV market penetration rates for the mixed traffic case.

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MVCM Car-Following Model for Connected Vehicles and Simulation-Based Traffic Analysis in Mixed Traffic Flow

Cited by 31 publications

References 29 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Cooperative predictive control for arbitrarily mixed vehicle platoons with guaranteed global optimality

Impact of Cooperative Adaptive Cruise Control on Traffic Stability

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