Fisher Identifiability Analysis of Longitudinal Vehicle Dynamics

TL;DR

This paper analyzes the fundamental limits of estimating vehicle longitudinal dynamics parameters using Fisher information, highlighting the impact of road grade variability on parameter identifiability through theoretical and experimental validation.

Contribution

It provides the first theoretical analysis of identifiability limits for vehicle parameters and validates these insights with real-world on-road data.

Findings

Road grade variability significantly affects parameter identifiability.

Theoretical bounds inform better experimental design.

Validation confirms the importance of road conditions in estimation accuracy.

Abstract

This paper investigates the theoretical Cramer-Rao bounds on estimation accuracy of longitudinal vehicle dynamics parameters. This analysis is motivated by the value of parameter estimation in various applications, including chassis model validation and active safety. Relevant literature addresses this demand through algorithms capable of estimating chassis parameters for diverse conditions. While the implementation of such algorithms has been studied, the question of fundamental limits on their accuracy remains largely unexplored. We address this question by presenting two contributions. First, this paper presents theoretical findings which reveal the prevailing effects underpinning vehicle chassis parameter identifiability. We then validate these findings with data from on-road experiments. Our results demonstrate, among a variety of effects, the strong relevance of road grade variability in determining parameter identifiability from a drive cycle. These findings can motivate improved experimental designs in the future.