Accuracy Evaluation of a Lightweight Analytic Vehicle Dynamics Model for Maneuver Planning

TL;DR

This paper evaluates a lightweight vehicle dynamics model designed for maneuver planning, analyzing how simplifying assumptions affect accuracy through real-world tests on tracks and public roads.

Contribution

It introduces a vehicle dynamics model supporting both analytic and generative approaches, with real-world validation and analysis of simplifying assumptions' effects.

Findings

Model performs well under typical conditions

Simplifying assumptions have quantifiable effects

Validated on both test track and public roads

Abstract

Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality criteria such as safety, comfort or ecology. Depending on the computation process, models with different assumptions and levels of detail are used; since maneuver planning usually has strong requirements for computation speed at a potentially high number of trajectory evaluations per planning cycle, most of the applied models aim to reduce complexity by implicitly or explicitly introducing simplifying assumptions. While evaluations show that these assumptions may be sufficiently valid under typical conditions, their effect has yet to be studied conclusively. We propose a model for vehicle dynamics that is convenient for maneuver planning by supporting both an analytic approach of extracting parameters from a given trajectory, and a generative approach of establishing a trajectory from given control inputs. Both applications of the model are evaluated in real-world test drives under dynamic conditions, both on a closed-off test track and on public roads, and effects arising from the simplifying assumptions are analyzed.