Resilient platooning control for connected automated vehicles under V2V communication disruptions

This study addresses the platooning control problem for connected automated vehicles, explicitly considering nonholonomic vehicle dynamics, curved roads, unknown platoon reference parameters, and intermittent jamming attacks on vehicle-to-vehicle (V2V) communication. Our central aim is to establish a discrete-time distributed cooperative control framework that emphasizes safety and energy efficiency as well as jamming resilience for the platoon. Toward this aim, an adaptive and resilient distributed estimator is first developed for each vehicle over a directed network topology, which robustly tracks unknown time-varying platoon reference and effectively operates under uncertain jamming, with formal guarantees on estimation error stability and maximal resilience. Second, a comprehensive discrete-time longitudinal and lateral platooning model is established to capture nonholonomic vehicle dynamics, curved road geometry, and uncertain reference trajectories. Third, a two-stage control scheme is devised such that both platoon safety and energy minimization can be preserved. This integrated scheme enables feasible longitudinal spacing regulation and lateral path following objectives, while simultaneously achieving jamming-resilient, collision-free, and energy-efficient maneuvering requirements. Stability of the resulting closed-loop platoon control system is formally proven. Numerical experiments are finally presented to demonstrate the effectiveness of the proposed approach across diverse curved-road driving and V2V communication scenarios.

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Publication Details

Journal
Robotics and Autonomous Systems
Published
2026-11-01
DOI
https://doi.org/10.1016/j.robot.2026.105838
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.00
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article

Resilient platooning control for connected automated vehicles under V2V communication disruptions

Xiaohua Ge, Tianrun Liu, Yang-Yang Chen
Robotics and Autonomous Systems
Traffic control and management
article

Resilient platooning control for connected automated vehicles under V2V communication disruptions

Xiaohua Ge, Tianrun Liu, Yang-Yang Chen
article en

Abstract

This study addresses the platooning control problem for connected automated vehicles, explicitly considering nonholonomic vehicle dynamics, curved roads, unknown platoon reference parameters, and intermittent jamming attacks on vehicle-to-vehicle (V2V) communication. Our central aim is to establish a discrete-time distributed cooperative control framework that emphasizes safety and energy efficiency as well as jamming resilience for the platoon. Toward this aim, an adaptive and resilient distributed estimator is first developed for each vehicle over a directed network topology, which robustly tracks unknown time-varying platoon reference and effectively operates under uncertain jamming, with formal guarantees on estimation error stability and maximal resilience. Second, a comprehensive discrete-time longitudinal and lateral platooning model is established to capture nonholonomic vehicle dynamics, curved road geometry, and uncertain reference trajectories. Third, a two-stage control scheme is devised such that both platoon safety and energy minimization can be preserved. This integrated scheme enables feasible longitudinal spacing regulation and lateral path following objectives, while simultaneously achieving jamming-resilient, collision-free, and energy-efficient maneuvering requirements. Stability of the resulting closed-loop platoon control system is formally proven. Numerical experiments are finally presented to demonstrate the effectiveness of the proposed approach across diverse curved-road driving and V2V communication scenarios.

Robotics and Autonomous Systems
Hohai University (CN), Swinburne University of Technology (AU), Southeast University (CN)
Openalex Percentile: Top 17%
Traffic control and management
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Resilient platooning control for connected automated vehicles under V2V communication disruptions — Xiaohua Ge, Tianrun Liu, et al. · Robotics and Autonomous Systems (2026) | TGRS Research Map | TGRS