Adaptive Longitudinal and Lateral Control for Autonomous Vehicles: High-Speed Platooning of Articulated Trucks

Abstract

Autonomous vehicle technology has seen remarkable advancements in recent years, yet significant challenges remain in ensuring robust, adaptive, and efficient control algorithms for diverse operational scenarios. This dissertation aims to address these challenges by developing and validating a generic control framework that is applicable to both independent autonomous vehicles and connected vehicle systems such as automated platoons. The versatility of the proposed framework ensures its applicability to a wide range of vehicles, including automobiles, light trucks, and rigid and articulated commercial trucks, under high-speed and complex driving conditions. The first major contribution is the development of a longitudinal control algorithm based on a nested PID structure. Designed for computational efficiency and stability, the algorithm simultaneously regulates vehicle speed and inter-vehicle distance. Its adaptability is extended to curved trajectories using an arc length-based error calculation, making it suitable for real-world scenarios. A rigorous simulation study is undertaken to demonstrate the algorithm's stability and robustness to parametric uncertainties. The second major contribution is the development of a high-speed lateral control algorithm based on a modified clothoid controller. This lateral control framework is designed to minimize lateral acceleration (improving passenger comfort and safety) and reduce cross-track errors (CTEs) across various vehicle configurations, including articulated trucks. Simulation results confirmed the superiority of the clothoid-based controller in minimizing CTEs and maintaining smooth steering profiles, even for complex vehicle configurations. Notably, tracking the steer axle center was found to significantly improve performance across all trajectory segments. The final contribution integrates the longitudinal and lateral control frameworks, enabling seamless operation in automated platooning scenarios. This integration requires adapting the longitudinal controller to curved trajectories using arc length-based calculations. Comprehensive simulations, including challenging trajectories such as dual lane changes, and actual roadways like sections of the Blue Ridge Parkway in Virginia and South Grade Road in California, validated the integrated framework. Despite minor anomalies in high-stress conditions, the results demonstrate acceptable performance in terms of spacing errors, relative velocities, lateral accelerations, and CTEs, highlighting the robustness and resilience of the proposed system. The study presents a unified control framework that bridges the gap between independent autonomous vehicles and connected vehicle systems. The generic nature of the algorithms ensures their applicability to a wide variety of vehicles and scenarios, making them a strong candidate for future deployment in autonomous systems. The findings represent significant advances toward safer, more efficient, and versatile autonomous vehicle technologies, addressing critical challenges in the path to commercial implementation