In its first two seasons, the Indy Autonomous Challenge (IAC) organized a series of autonomous racing events across some of the most renowned oval racetracks, introducing various challenges including high-speed solo runs, static obstacle avoidance, and complex head-to-head passing competitions. In 2023, the challenge expanded to include a time-trial event on the iconic F1 Monza road course. This article outlines the complete software architecture utilized by team TII Unimore Racing (formerly TII EuroRacing), er.autopilot 1.1, encompassing all modules necessary for static obstacle avoidance, active overtakes, achieving speeds over 75 m/s (270 km/h), and navigating complex road course tracks. Building on the previous version, this updated stack integrates new features such as LiDAR-based localization, lateral velocity estimation, a radar-based local controller for safe pull-overs, and refined vehicle modeling for the model predictive controller. We present the overall results along with insights and lessons learned from the first two seasons, during which the team consistently achieved the podium.

Raji, A., Caporale, D., Gatti, F., Toschi, A., Musiu, N., Verucchi, M., et al. (2024). er.autopilot 1.1: A Software Stack for Autonomous Racing on Oval and Road Course Tracks. IEEE TRANSACTIONS ON FIELD ROBOTICS, 1, 332-359 [10.1109/tfr.2024.3501252].

er.autopilot 1.1: A Software Stack for Autonomous Racing on Oval and Road Course Tracks

Toschi, Alessandro;Prignoli, Francesco;
2024

Abstract

In its first two seasons, the Indy Autonomous Challenge (IAC) organized a series of autonomous racing events across some of the most renowned oval racetracks, introducing various challenges including high-speed solo runs, static obstacle avoidance, and complex head-to-head passing competitions. In 2023, the challenge expanded to include a time-trial event on the iconic F1 Monza road course. This article outlines the complete software architecture utilized by team TII Unimore Racing (formerly TII EuroRacing), er.autopilot 1.1, encompassing all modules necessary for static obstacle avoidance, active overtakes, achieving speeds over 75 m/s (270 km/h), and navigating complex road course tracks. Building on the previous version, this updated stack integrates new features such as LiDAR-based localization, lateral velocity estimation, a radar-based local controller for safe pull-overs, and refined vehicle modeling for the model predictive controller. We present the overall results along with insights and lessons learned from the first two seasons, during which the team consistently achieved the podium.
2024
Raji, A., Caporale, D., Gatti, F., Toschi, A., Musiu, N., Verucchi, M., et al. (2024). er.autopilot 1.1: A Software Stack for Autonomous Racing on Oval and Road Course Tracks. IEEE TRANSACTIONS ON FIELD ROBOTICS, 1, 332-359 [10.1109/tfr.2024.3501252].
Raji, Ayoub; Caporale, Danilo; Gatti, Francesco; Toschi, Alessandro; Musiu, Nicola; Verucchi, Micaela; Prignoli, Francesco; Malatesta, Davide; Jesus, ...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1001231
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