Eugenio Tramacere

The suspension system in plays a pivotal role, especially in off-road vehicles, in ensuring optimal comfort, road holding and ride safety. This study explores the transition from a MacPherson strut to a double wishbone suspension system, emphasizing its impact on relevant suspension features, such as camber and caster angles, motion ratio and vertical dynamics. Through this study, an off-road vehicle was retrofitted with the proposed suspension architecture and tested both numerically and experimentally. Test results reproduce simulation outcomes, thus confirming the effectiveness of the redesigned suspension system for the target vehicle, especially for demanding off-road applications.

Nowadays battery electric vehicles (BEVs) are seen as a promising and viable alternative to traditional road transport means. However, these vehicles weight more than conventional ones due to the presence of bulky battery packs. This work presents the design of a rear-wheel steering (RWS) adaptive Linear Quadratic Regulator (LQR) control capable of improving handling characteristics of the retained electric vehicle. The reliability of the control is highlighted by performing manoeuvres at different road friction coefficients. Moreover, the RWS controller is validated by implementing a processor-in-the-loop (PIL) architecture in a high-performance real-time target computing machine.

Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation.