Adaptive Event-triggered Formation Control of Autonomous Vehicles

Wang, Ziming; Zhang, Yihuai; Zhao, Chenguang; Yu, Huan · 2025 · ArXiv.org

DOI: 10.48550/arxiv.2506.06746

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Summary

This paper addresses the challenge of adaptive event-triggered formation control for autonomous vehicles (AVs) operating under longitudinal and lateral motion uncertainties. While traditional platoon control focuses on longitudinal regulation within fixed single-lane formations, this work expands to formation control that integrates both longitudinal and lateral dynamics. This expansion enables AVs to navigate complex traffic scenarios, including narrow passages, collaborative obstacle avoidance, and cut-in maneuvers. The research is motivated by the limitations of conventional strategies that rely on predefined communication topologies and continuous state transmission, which are vulnerable to communication disruptions and resource inefficiencies. The proposed framework utilizes a sampling-based observer to reconstruct vehicle dynamics from inaccurate position data collected by radar and lidar sensors, thereby reducing the need for continuous inter-vehicle communication. The control design employs an adaptive backstepping continuous-time controller augmented with radial basis function neural networks to approximate unknown nonlinear drag factors and disturbances. To optimize resource usage, the authors design three distinct event-triggered mechanisms (ETC) that update control signals aperiodically based on specific thresholds: a fixed-threshold strategy, a relative-threshold strategy that scales with the control signal magnitude, and a switched-threshold strategy that combines elements of both. Lyapunov-based stability analysis is conducted to guarantee bounded tracking errors and prevent Zeno behavior. The study validates the proposed controllers through simulations across three specific scenarios: linear formation for navigating narrow passages, square formation for collaborative obstacle avoidance, and linear-queue formation for accommodating cut-in vehicles. The results demonstrate that the adaptive event-triggered controllers effectively maintain formation stability and tracking performance despite motion uncertainties and sensor inaccuracies. The analysis highlights the trade-offs between formation tracking precision and control efficiency among the three ETC strategies. Specifically, the switched-threshold strategy is shown to balance computational load and tracking performance by allowing longer update intervals during significant maneuvers while ensuring precise control near equilibrium. The significance of this work lies in its comprehensive approach to vehicular formation control that accounts for both longitudinal and lateral dynamics without relying on robust communication connectivity. By employing sampling-based observers and adaptive event-triggered mechanisms, the framework enhances the scalability, safety, and mobility of AV formations in diverse and complex environments. The findings provide a rigorous framework for implementing continuous-in-time controllers on digital platforms, offering practical solutions for improving traffic efficiency and safety in intelligent transportation systems.

Key finding

The proposed adaptive event-triggered formation control framework successfully maintains bounded tracking errors and avoids Zeno behavior while reducing control update frequency across linear, square, and linear-queue vehicular formations.

Methodology

simulation_modeling

Provenance

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