Longitudinal-Motion-Aware Lateral Control for Autonomous Vehicles: A Robust Nonlinear Control Framework

2026-07-02

AI Summary

This work addresses the limitations of existing autonomous vehicle lateral controllers, which often rely on assumptions of constant speed or fixed operating points and thus struggle with longitudinal dynamics and vehicle parameter uncertainties. The authors propose a robust nonlinear lateral control framework that explicitly incorporates longitudinal motion states. By constructing a tracking error model dependent on both longitudinal velocity and acceleration, and employing feedback linearization for precise lateral tracking, the approach ensures stability through careful analysis of internal dynamics. Innovatively embedding time-varying longitudinal information into the lateral control law, two robust strategies are developed—based on Lyapunov redesign (LR) and incremental nonlinear dynamic inversion (INDI)—with key tuning parameters identified and ultimate boundedness guaranteed. Simulations and real-vehicle experiments demonstrate high tracking accuracy, strong robustness under varying speed and acceleration conditions, and effective real-time deployability.

Abstract

As autonomous vehicles (AVs) operate in increasingly dynamic traffic conditions, lateral control must be performed while longitudinal speed and acceleration vary. Yet many existing lateral controllers rely on constant-speed or operating-point-based assumptions, which can degrade performance during transient longitudinal maneuvers. Moreover, most methods assume precisely known vehicle parameters, despite real-world parametric uncertainties. To address these limitations, this paper presents a longitudinal-motion-aware robust nonlinear lateral control framework for AVs. It first derives a tracking error model that depends on varying longitudinal speed and acceleration. Using this model, feedback linearization is employed to obtain a linear input-output relation for lateral error tracking while embedding longitudinal motion into the control law. The resulting internal dynamics are then analyzed to ensure overall system stability. To address parameter uncertainty, two robust control designs with distinct implementation trade-offs are proposed: (i) a Lyapunov redesign (LR) approach inspired by sliding mode control, and (ii) an incremental nonlinear dynamic inversion (INDI) method. Both are rigorously analyzed and proven to ensure ultimate boundedness, with key robustness-tuning parameters explicitly identified. Simulations demonstrate enhanced tracking accuracy, consistent performance across varying speeds and accelerations, and robustness to model uncertainties, while also examining the effects of the robustness-related parameters. Real-vehicle tests further confirm real-time implementation and practical path-tracking performance on actual hardware.

Problem

• lateral control
• longitudinal motion
• parametric uncertainty
• autonomous vehicles
• nonlinear control

Innovation

• longitudinal-motion-aware
• robust nonlinear control
• feedback linearization
• Lyapunov redesign
• incremental nonlinear dynamic inversion