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.

翻译：本文提出了一种新颖的感知运动学习框架，该框架与主动推理相结合，旨在增强高度自动化车辆的横向控制能力。受人类运动学习启发，感知运动学习强调感知与行动的无缝整合，从而在动态环境中实现高效决策。传统的自动驾驶方法——包括模块化流水线、模仿学习和强化学习——在适应性、泛化能力和计算效率方面存在不足。相比之下，结合主动推理的感知运动学习利用生成模型来最小化预测误差，并基于学习到的感知-运动表征主动塑造车辆控制。我们的方法将深度学习与主动推理原理相统一，使得高度自动化车辆能够以最少的数据执行车道保持操作，且无需在不同环境中进行大量重新训练。在CARLA模拟器中进行的大量实验表明，结合主动推理的感知运动学习在不增加计算开销的情况下增强了适应性，同时实现了与传统方法相当的性能。这些发现凸显了感知运动学习驱动的主动推理作为现实世界自动驾驶应用的一种鲁棒替代方案的潜力。