Robust Control of Cooperative Adaptive Cruise Control in the Absence of Information About Preceding Vehicle Acceleration

Cooperative adaptive cruise control (CACC) has a strong potential to improvise highway traffic capacity and ease traffic disturbances. Extensive exploration is not carried out in the area of CACC for a cut-in maneuver. Contemporary control strategies proposed for CACC cannot regulate the peaking of control input and thus the acceleration/deceleration of following vehicles when applied for various real traffic scenarios. This paper aims to develop a non-linear disturbance observer-based sliding mode control to control a CACC system for various traffic scenarios. The proposed observer estimates the uncertainty present in the actuator dynamics and the preceding vehicle’s acceleration as the lumped disturbance at the same time, it adjusts the observer gain to alleviate the peaking of control input. The stability of individual vehicles and the string stability of vehicle platoon are derived The performance of the proposed scheme is validated with various traffic scenarios, that is, cut-in maneuver, cut-out maneuver, and non-zero initial conditions. The effectiveness of the proposed scheme is demonstrated by comparing it with a linear disturbance observer-based control.

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“…In Sawant et al, 26 a linear DO based SMC is derived for a platoon of vehicles. The sliding variable and the control law are given by…”

Section: Simulation Resultsmentioning

confidence: 99%

A non-linear disturbance observer-based cooperative adaptive cruise control for real traffic scenarios

Sawant

Chaskar

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…CCAM mainly includes the technology of vehicular communications, automated driving, and cooperating transportation systems. Recently, numerical research proposed a series of innovative technology for CCAM, such as 5G-SDN-MPTCP-based communication systems [45], multi-tier orchestration platform architectures [46], safety-related assessment platforms [47], and a significant number of cooperative adaptive cruising control algorithms [48,49]. There are also some evaluation and incentive systems related to the social and economic benefits of CCAM.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

Wang

2022

Algorithms

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Capacity drop is the critical phenomenon that triggers traffic congestion, while traffic evolution is very complex during a capacity drop. This study applied the empirical vehicle trajectory data to explore the traffic characteristics during the capacity drop at the tunnel bottleneck section. We first construct a capacity drop analysis model using image processing technology to extract high-precision vehicle trajectories. We then analyze the characteristics of the evolution process of the capacity drop at the bottleneck area. The results show that the capacity drop is a dynamic evolution process from free flow to congested flow where traffic operation is distinct. The capacity drop shows the difference between congested flow and non-congested flow. The driving characteristics of drivers in the two states are also different. The influence of lane-changing behavior on the capacity drop is estimated. In the free flow state, the disturbance caused by lane-changing can be quickly eliminated. With the increase in vehicle numbers in the area, the frequent lane-changing behavior accumulates disturbance. When the disturbance reaches a certain degree, congestion will occur, and the vehicle’s speed will drop sharply, resulting in a capacity drop.

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“…By applying H ∞ control tools to the both plant stability and string stability of the average error dynamics and minimizing the variance of the trajectories, the effect of the communication losses can be effectively mitigated. A disturbance observerbased sliding mode control is proposed in [36], which estimates the uncertainty present in the actuator dynamics and the acceleration of the preceding vehicle as a lumped disturbance. In addition, state estimation is considered to be an effective solution to temporary loss of communication [37].…”

Section: Introductionmentioning

confidence: 99%

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

Xie

Ding

et al. 2023

IEEE Trans. Veh. Technol.

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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.

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Robust Control of Cooperative Adaptive Cruise Control in the Absence of Information About Preceding Vehicle Acceleration

Cited by 59 publications

References 44 publications

A non-linear disturbance observer-based cooperative adaptive cruise control for real traffic scenarios

A non-linear disturbance observer-based cooperative adaptive cruise control for real traffic scenarios

Tunnel Traffic Evolution during Capacity Drop Based on High-Resolution Vehicle Trajectory Data

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

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