Abstract
In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safetycritical situations, where the environmental conditions (rain, snow, ice), the road singularities (oil stains, puddles, holes), and the tyre thermal and ageing phenomena can deeply affect the adherence potential. The objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm in a hardware-in-the-loop real-time environment and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. The multidisciplinary proposal will provide a paradigm shift in the development of strategies and a solid breakthrough towards enhanced development of the driving automatization systems, unleashing the potential of physical modeling to the next level of vehicle control, able to exploit and to take into account the multi-physical tyre variations.
Highlights
The information concerning the vehicle’s non-linear physical limits depending on the thermal and wear states of tyres, the pavement characteristics, and the boundary conditions represents a fundamental additional value for the optimal behavior of safety- and performance-oriented control logics [1,2,3].Virtual driver prototyping is becoming an increasingly exploited tool, allowing the car manufacturer to perform the majority of the testing campaign already in the design phase of the vehicle
The most recent virtual driver (VD) implementations rely on a vehicle controller based on a non-linear model predictive control (NMPC) technique, which is a model-based control strategy able to compute the optimal sequence of control inputs over a prediction horizon, by minimizing a tailored cost function [8,9]
To investigate the possible outcomes of a model-based control within a vehicle safety-linked scenario, the authors have performed within the double lane change (DLC) maneuver a complete design of experiment comprehending:
Summary
The information concerning the vehicle’s non-linear physical limits depending on the thermal and wear states of tyres, the pavement characteristics, and the boundary conditions (wet or icy ground, under-inflated or worn tyre, etc.) represents a fundamental additional value for the optimal behavior of safety- and performance-oriented control logics [1,2,3].Virtual driver prototyping is becoming an increasingly exploited tool, allowing the car manufacturer to perform the majority of the testing campaign already in the design phase of the vehicle. Specific prototyping choices can be reproduced and evaluated in any condition within the virtual environment, at the limit of performance, minimizing the time-to-market and connected costs [4,5]. In this field, closed-loop control strategies have been widely studied in past years to address the problem of path following for autonomous driving cars. The most recent VD implementations rely on a vehicle controller based on a non-linear model predictive control (NMPC) technique, which is a model-based control strategy able to compute the optimal sequence of control inputs over a prediction horizon, by minimizing a tailored cost function [8,9]. The control technique is applied in a receding horizon mode and is capable of handling constraints and the intrinsic non-linearities of the vehicle model [10]
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