Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control
This work addresses adaptability and generalization issues in autonomous driving for real-world applications, though it appears incremental as it combines existing concepts like deep learning with active inference.
This paper tackled the problem of enhancing lateral control in Highly Automated Vehicles by integrating Perceptual Motor Learning with Active Inference, achieving performance comparable to conventional methods with minimal data and no extensive retraining across different environments in CARLA simulator experiments.
This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, inspired by human motor learning, emphasizes the seamless integration of perception and action, enabling efficient decision-making in dynamic environments. Traditional autonomous driving approaches--including modular pipelines, imitation learning, and reinforcement learning--struggle with adaptability, generalization, and computational efficiency. In contrast, PML with AIF leverages a generative model to minimize prediction error ("surprise") and actively shape vehicle control based on learned perceptual-motor representations. Our approach unifies deep learning with active inference principles, allowing HAVs to perform lane-keeping maneuvers with minimal data and without extensive retraining across different environments. Extensive experiments in the CARLA simulator demonstrate that PML with AIF enhances adaptability without increasing computational overhead while achieving performance comparable to conventional methods. These findings highlight the potential of PML-driven active inference as a robust alternative for real-world autonomous driving applications.