The Role of Trajectory Planners in Lane Change Tracking Control: A Monte Carlo Evaluation of Four Controllers under Uncertainty

In the realm of autonomous transportation, executing vehicle manoeuvres accurately and reliably is paramount. The usual separation of trajectory planning and tracking, due to diverse origins and complexities, can lead to limitations in the closed-loop tracking behaviour. Nonlinear vehicle dynamics, stemming from nonholonomic constraints and tyre-road interactions demand simplified models for realtime planning. This paper comprehensively evaluates the combination of three trajectory planners with four tracking controllers using Monte Carlo analysis, considering scenario and model uncertainties. While planners use simplified or no models, tracking controllers based on nominal models can deviate due to uncertain parameters and environmental variations. Our study systematically evaluates tracking performance under uncertainty, supposing feasible planned trajectories adhering to physics-based constraints. We explore tracking error consistency across trajectory planners, assessing if feasibility alone can limit tracking errors.