Automation of road vehicles faced a disruptive growth in the last decades and it's expected to deeply change many aspects of the society. In last years we assisted to a spread of autonomous driving pilot projects and proof of concepts by means of experimental prototypes both from companies and research groups. In this paper, a trajectory planner for autonomous driving is implemented and tested on a prototype vehicle. A full description of Model predictive control (MPC) formulation adopted is provided and implementation choices are motivated. A performance and reliability analysis is presented by means of a comparison between simulation and experimental results collected on a racetrack. Conclusions and future development directions are drawn.

Experimental Implementation of a Trajectory Planner for Autonomous Driving

Arrigoni, Stefano;Pirovano, Matteo;Mentasti, Simone;Khayyat, Michael;Braghin, Francesco;Cheli, Federico;Matteucci, Matteo
2022-01-01

Abstract

Automation of road vehicles faced a disruptive growth in the last decades and it's expected to deeply change many aspects of the society. In last years we assisted to a spread of autonomous driving pilot projects and proof of concepts by means of experimental prototypes both from companies and research groups. In this paper, a trajectory planner for autonomous driving is implemented and tested on a prototype vehicle. A full description of Model predictive control (MPC) formulation adopted is provided and implementation choices are motivated. A performance and reliability analysis is presented by means of a comparison between simulation and experimental results collected on a racetrack. Conclusions and future development directions are drawn.
2022
2022 AEIT International Annual Conference, AEIT 2022
978-88-87237-55-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1227607
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