In this paper we present a robust controller design methodology for vehicle rollover prevention utilizing active steering. Control design is based on keeping the magnitude of the vehicle load transfer ratio (LTR) below a certain level in the presence of driver steering inputs; we also develop an exact expression for LTR. The proposed controllers have a proportional-integral structure whose gain matrices are obtained using the results of Pancake et al. ( 2000 Pancake, T , Corless, M and Brockman, M . June 2000. “Analysis and control of polytopic uncertain/nonlinear systems in the presence of bounded disturbance inputs”. In Proceedings of the American Control Conference June, 159–163. Chicago, IL [Google Scholar] ) (T. Pancake, M. Corless and M. Brockman, “Analysis and control of polytopic uncertain/nonlinear systems in the presence of bounded disturbance inputs”, Proceedings of the American Control Conference, Chicago, IL, June 2000; T. Pancake, M. Corless and M. Brockman, “Analysis and control for a class of uncertain/nonlinear systems in the presence of bounded disturbance inputs”, 2007). These controllers reduce the transient magnitude of the LTR while maintaining the steady state steering response of the vehicle. The controllers can be designed to be robust with respect to vehicle parameters such as speed and centre of gravity height. We also provide a modification to the controllers so that they only activate when the potential for rollover is significant. Numerical simulations demonstrate the efficacy of our approach and the resulting controllers.

In this paper, we present a methodology based on multiple models and switching for real-time estimation of centre of gravity (CG) position in automotive vehicles. The method utilises well-known simple linear vehicle models for lateral and roll dynamics and assumes the availability of standard stock automotive sensors. We illustrate the technique with numerical simulations as well as with measured sensor data from an sport utility vehicle. We also compare our estimation results with traditional linear least-squares estimators to show the efficacy of our technique. Finally, we give a simple application example for implementing the idea in automotive vehicles as a switch for rollover controller activation.

The purpose of this study is to design high-performance active braking control and observer algorithms for passenger vehicles equipped with electromechanical brake systems. These algorithms are designed to be adaptive with changing driving and road conditions in a switched multiple-model manner to ensure high performance and robustness. The effectiveness of a set of multiple-model switching lead-lag controllers is evaluated during transitions between different road friction coefficients. Meanwhile, a multiple-model switching observer algorithm is developed to estimate the shape of the tyre braking force curve with respect to the longitudinal slip. Each switched observer predicts signals according to its preset tyre model. The observers are designed based on different Burckhardt tyre models that are parameterized for different road conditions. In our simulations, the value of the friction coefficient is assumed to be unknown and our switching algorithms are observed to estimate successfully the varying friction coefficients by comparing a quadratic cost function of measured signals from the vehicle with signals generated by observers. We demonstrate that our algorithms provide high reliability and fast response, thus ensuring a stopping distance close to the theoretical minimum.

A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

In this paper, we apply recent results from robust control to the problem of rollover prevention in automotive vehicles. Specifically, we exploit the results of Pancake, Corless and Brockman, which provide controllers to robustly guarantee that the peak values of the performance outputs of an uncertain system do not exceed certain values. We introduce a new measure of performance for rollover prevention, the load transfer ratio LTR_d, and design differential-braking-based rollover controllers to keep the value of this quantity below a certain level; we also obtain controllers which yield robustness to variations in vehicle speed. We present numerical simulations to demonstrate the efficacy of our controllers