I-RIM Conference 2019 October 18 - 20, Rome, Italy ISBN: 9788894580501 Towards the Design of Robotic Drivers for Full-Scale Self-Driving Racing Cars Danilo Caporale∗,†, Alessandro Settimi†, Federico Massa†, Luca Bonamini‡, Manuel G. Catalano‡‡, Antonio Bicchi†,‡,‡‡ and Lucia Pallottino†,‡ Abstract— We report on the development of a planning and control framework for an electric full scale autonomous racing car. We describe the whole control system architecture including the mapping and localization methods employed. We also report on novel experimental results obtained within the Roborace Localization challenge, whose ﬁrst edition took place in Zala Zone1 track in August 2019, related to an informed version of Adaptive Monte Carlo Localization developed for the racing scenario. I. AUTONOMOUS RACING SYSTEMS Challenges are one of the main drives of robotics devel- opment, that call for integration of recent research results in real world applications. In the particular case of self-driving cars, the DARPA Grand Challenge and Urban Challenge have pushed the robotics community to develop autonomous cars meant to drive in unstructured or urban environments. Following the encouraging results of these challenges, car producers have started developing self-driving technologies, which are foreseen to become a disruptive reality in the next decade and possibly the ﬁrst widespread Artiﬁcial Intelligence (AI) applications in robotics. From deploying single vehicles to the consumer market to ﬂeets of shared vehicles to be used as a service, there is a strong interest in the development of level 4 and 5 autonomous behaviors for these cars. Of course, to achieve that, human safety is one of the main concerns. In fact, one of the expected results of the mass adoption of these systems is a sensible reduction of the rate of accidents, especially those due to the errors of human drivers. Despite this being a reasonable outcome, at the moment of writing this paper it is not yet clear where we stand with respect to this goal, and extensive testing would be necessary to conﬁrm that this goal is within reach. In this work, we focus on the particular scenario of a racing track environment, which allows driverless vehicles testing in extreme conditions (high accelerations that trigger the nonlinear behavior of vehicles) without jeopardizing human safety. The interest in this application is justiﬁed by the recent creation of racing challenges for full scale (Roborace2), middle scale (Formula SAE3), and small scale ∗Corresponding author: d.caporale@centropiaggio.unipi.it † Centro di Ricerca E. Piaggio - Universit`a di Pisa - Pisa, Italy ‡ Universit`a di Pisa - Pisa, Italy ‡‡ Istituto Italiano di Tecnologia - Genova, Italy 1https://zalazone.hu/en/ 2https://roborace.com/ 3https://www.fsaeonline.com/ Fig. 1. Roborace DevBot during the ﬁrst Localisation Challenge - Zala Zone (Hungary) 2019. (F1Tenth4) vehicles. Autonomous racing capabilities for non- electric vehicles have been demonstrated but big challenges remain open in improving the self-driving capabilities of full scale electric racing cars. The experimental results we present were obtained on a Roborace’s DevBot, the development version of Robocar (see Fig. 1). The car can be driven either by a human pilot or by an autonomous system. The control architecture of the vehicle is hierarchical and it includes, at low-level, fast response control-loops implemented by the manufacturer, ensuring closed loop control of the actuators and raw input processing from the on-board sensors, while exposing at higher level an open platform for the implementation of user-deﬁned control strategies and perception schemes, see Fig. 2. This key feature ﬁrst enables rapid development and testing of full-stack autonomous driving control solutions, in which the designer can focus on driverless technology. It also seamlessly allow for the implementation of learning-based approaches in which the human or autonomous driver can