用感知-运动学习框架提升自动驾驶车道保持的适应性。
Perceptual Motor Learning with Active Inference Framework for Robust Lateral Control
- 融合主动推理的感知-运动学习，统一感知与控制
- 仅需少量数据即可实现跨环境自适应，性能媲美传统方法
- 计算开销不变，适合实时自动驾驶系统
本文提出一种结合主动推理（Active Inference, AIF）的感知-运动学习（Perceptual Motor Learning, PML）框架，用于提升高度自动化车辆（HAVs）的横向控制能力。PML受人类运动学习启发，强调感知与动作的无缝融合，可在动态环境中实现高效决策。传统自动驾驶方法（如模块化流水线、模仿学习、强化学习）在适应性、泛化能力和计算效率方面存在局限。本方法利用生成模型最小化预测误差（“意外”），并基于学习到的感知-运动表征主动调整车辆控制。该框架将深度学习与主动推理原则融合，使HAV在无需大量重训练的情况下，在不同环境下完成车道保持任务。在CARLA仿真器中的大量实验表明，PML-AIF在不增加计算开销的前提下显著提升适应性，性能可与传统方法相当。结果表明，基于PML的主动推理具有成为真实世界自动驾驶鲁棒替代方案的巨大潜力。
原文摘要 · Abstract (English)
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.
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