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arXiv · 2409.18097

A Sim-to-Real Vision-based Lane Keeping System for a 1:10-scale Autonomous Vehicle

Antonio Gallina·Matteo Grandin·Angelo Cenedese·Mattia Bruschetta
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Abstract

In recent years, several competitions have highlighted the need to investigate vision-based solutions to address scenarios with functional insufficiencies in perception, world modeling and localization. This article presents the Vision-based Lane Keeping System (VbLKS) developed by the DEI-Unipd Team within the context of the Bosch Future Mobility Challenge 2022. The main contribution lies in a Simulation-to-Reality (Sim2Real) GPS-denied VbLKS for a 1:10-scale autonomous vehicle. In this VbLKS, the input to a tailored Pure Pursuit (PP) based control strategy, namely the Lookahead Heading Error (LHE), is estimated at a constant lookahead distance employing a Convolutional Neural Network (CNN). A training strategy for a compact CNN is proposed, emphasizing data generation and augmentation on simulated camera images from a 3D Gazebo simulator, and enabling real-time operation on low-level hardware. A tailored PP-based lateral controller equipped with a derivative action and a PP-based velocity reference generation are implemented. Tuning ranges are established through a systematic time-delay stability analysis. Validation in a representative controlled laboratory setting is provided.

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Antonio Gallina, Matteo Grandin, Angelo Cenedese, Mattia Bruschetta. 2024-09-26. A Sim-to-Real Vision-based Lane Keeping System for a 1:10-scale Autonomous Vehicle. https://arxiv.org/abs/2409.18097

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