Processor-in-the-Loop Architecture Design and Experimental Validation for an Autonomous Racing Vehicle

Abstract

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.

Hardware‐in‐the‐loop validation of an adaptive model predictive control on a connected and automated vehicle

“…In order to check the effectiveness of the proposed control strategy and to contrast the obtained results with previously published methods exploiting the MPC approach, a comparison with the adaptive MPC based on a simpler prediction model in References 36‐38 is performed. This choice depends on the similarity with the proposed prediction model that, however, is structurally more complex.…”

“…[33][34][35] From the state-of-the-art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control. In particular, most of the recent published methods concern predictive control strategies for ADAS and Automated Vehicles (AVs), mainly adaptive [36][37][38][39] and nonlinear. 40,41 The Adaptive MPC is considered one of the most suitable control strategies for CAVs, since it allows the vehicle to cope with time-varying parameters also during the prediction horizon.…”

“…From the state‐of‐the‐art literature, MPC results to be the most applied control strategy, used for both cooperative purposes and for longitudinal/lateral vehicle control. In particular, most of the recent published methods concern predictive control strategies for ADAS and Automated Vehicles (AVs), mainly adaptive 36‐39 and nonlinear 40,41 …”

A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and Perspectives

“…[55]- [57], [59], [62]- [68], [71], [73]- [77], [82]- [84], [88] Electric motor traction modeling [34], [37]- [42], [45], [56], [67], [68], [70], [82], [83],…”

“…[54]- [57], [59], [62], [64]- [70], [72], [80], [82], [83], [88], [90], [91] Rolling resistance modeling [32]- [35], [38], [40], [42],…”