Autonomous Vehicle Platform

Autonomous vehicle platforms are being developed and refined across various scales, from small-scale educational models like F1TENTH to full-size vehicles, with a primary objective of creating safe and reliable autonomous driving systems. Current research emphasizes developing and validating control algorithms, often employing model predictive control (MPC), reinforcement learning (RL), and computer vision techniques like semantic segmentation for real-time perception and decision-making. These efforts leverage digital twin frameworks for simulation and testing across different scales and operational domains, contributing to advancements in both autonomous driving technology and the broader field of robotics and control systems.