Uncertainties in Robust Planning and Control of Autonomous Tractor-Trailer Vehicles

TL;DR

This paper investigates how uncertainties affect the motion planning and control of autonomous tractor-trailer vehicles, using sensitivity analysis and simulations to quantify impacts and suggest robust control strategies.

Contribution

It introduces a comprehensive analysis of uncertainties in an 8-DOF tractor-semitrailer model, highlighting their varying significance and proposing methods for robust prediction and control.

Findings

Uncertainty impacts vary with scenario.

Sampling-based predictions provide bounds on trajectories.

Potential applications include robust control and driver assistance.

Abstract

To study the effects of uncertainty in autonomous motion planning and control, an 8-DOF model of a tractor-semitrailer is implemented and analyzed. The implications of uncertainties in the model are then quantified and presented using sensitivity analysis and closed-loop simulations. The analysis reveals that the significance of various model parameters varies depending on the specific scenario under investigation. By using sampling-based closed-loop predictions, uncertainty bounds on state variable trajectories are determined. Our findings suggest the potential for the inclusion of our method within a robust predictive controller or as a driver-assistance system for rollover or lane departure warnings.