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

TL;DR

The main contribution lies in a Simulation-to-Reality (Sim2Real) GPS-denied VbLKS for a 1:10-scale autonomous vehicle and a training strategy for a compact CNN employing a Convolutional Neural Network (CNN).

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.

Figures (21)

• Figure 1: DEI-Unipd Team's vehicle during BFMC 2022
• Figure 2: Block scheme of the Lane Keeping System architecture
• Figure 3: Block scheme of the proposed VbLKS
• Figure 4: Bosch Future Mobility Challenge 2022 scenario
• Figure 5: DEI-Unipd Team's 1:10 scale vehicle