Longitudinal Vehicle Dynamics: A Comparison of Physical and Data-Driven Models Under Large-Scale Real-World Driving Conditions

Sebastian S. James,Mauro Da Lio,Sean R. Anderson

doi:10.1109/access.2020.2988592

Sebastian S. James, Mauro Da Lio + Show 1 more

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https://doi.org/10.1109/access.2020.2988592

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Abstract

Mathematical models of vehicle dynamics will form essential components of future autonomous vehicles. They may be used within inverse or forward control loops, or within predictive learning systems. Often, nonlinear physical models are used in this context, which, though conceptually simple (especially for decoupled, longitudinal dynamics), may be computationally costly to parameterise and also inaccurate if they omit vehicle-specific dynamics. In this study we sought to determine the relative merits of a commonly used nonlinear physical model of vehicle dynamics versus data-driven models in large-scale real-world driving conditions. To this end, we compared the performance of a standard nonlinear physical model with a linear state-space model and a neural network model. The large-scale experimental data was obtained from two vehicles; a Lancia Delta car and a Jeep Renegade sport utility vehicle. The vehicles were driven on regular, public roads, during normal human driving, across a range of road gradients. Both data-driven models outperformed the physical model. The neural network model performed best for both vehicles; the state-space model performed almost as well as the neural network for the Lancia Delta, but fell short for the Jeep Renegade whose dynamics were more strongly nonlinear. Our results suggest that the linear data-driven model gives a good trade-off in accuracy and simplicity, whilst the neural network model is most accurate and is extensible to more nonlinear operating conditions, and finally that the widely used physical model may not be the best choice for control design.

Highlights

Driver assistance systems such as cruise control, adaptive cruise control [1], [2], vehicle platooning schemes [3]–[5] and future driverless cars [6]–[9] all depend on mathematical models of vehicle dynamics
We focus on modelling longitudinal vehicle dynamics in the range of conditions that are realised by ordinary human drivers, as better delimited in Section II.A; on the longitudinal vehicle velocity
The nonlinear physical model parameters were estimated as kD = 0.2777, kR = 0.0101 and kτ = 9.469 for the Lancia Delta, and as kD = 2.415, kR = 0.00023 and kτ = 120.9 for the Jeep Renegade

Summary

Introduction

Driver assistance systems such as cruise control, adaptive cruise control [1], [2], vehicle platooning schemes [3]–[5] and future driverless cars [6]–[9] all depend on mathematical models of vehicle dynamics. Derived models are commonly employed for these applications; examples can be found for lane change manoeuvres [10], [11], lane keeping [12]–[14] and cruise control [2]–[5]. The development of low-level longitudinal and lateral vehicle control algorithms, e.g. using model predictive control (MPC), has been based on physically derived models, either. This paper focuses on the development of vehicle dynamics models for control design, where the key objective is model parsimony, i.e. the model should be no more complicated than needed for the purpose of control design.

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