This paper presents a novel approach to Autonomous Vehicle (AV) control through the application of active inference, a theory derived from neuroscience that conceptualizes the brain as a predictive machine. Traditional autonomous driving systems rely heavily on Modular Pipelines, Imitation Learning, or Reinforcement Learning, each with inherent limitations in adaptability, generalization, and computational efficiency. Active inference addresses these challenges by minimizing prediction error (termed "surprise") through a dynamic model that balances perception and action. Our method integrates active inference with deep learning to manage lateral control in AVs, enabling them to perform lane following maneuvers within a simulated urban environment. We demonstrate that our model, despite its simplicity, effectively learns and generalizes from limited data without extensive retraining, significantly reducing computational demands. The proposed approach not only enhances the adaptability and performance of AVs in dynamic scenarios but also aligns closely with human-like driving behavior, leveraging a generative model to predict and adapt to environmental changes. Results from extensive experiments in the CARLA simulator show promising outcomes, outperforming traditional methods in terms of adaptability and efficiency, thereby advancing the potential of active inference in real-world autonomous driving applications.

翻译：本文提出了一种通过应用主动推理实现自动驾驶车辆控制的新方法。主动推理是一种源自神经科学的理论，它将大脑概念化为一种预测机器。传统的自动驾驶系统严重依赖于模块化流水线、模仿学习或强化学习，这些方法在适应性、泛化能力和计算效率方面均存在固有局限。主动推理通过一个平衡感知与行动的动态模型来最小化预测误差（称为"惊奇"），从而应对这些挑战。我们的方法将主动推理与深度学习相结合，以管理自动驾驶车辆的横向控制，使其能够在模拟城市环境中执行车道跟随操作。我们证明，尽管模型结构简单，但它能够从有限数据中有效学习并实现泛化，无需大量重新训练，从而显著降低了计算需求。所提出的方法不仅增强了自动驾驶车辆在动态场景中的适应性和性能，而且通过利用生成模型来预测和适应环境变化，使其行为与类人驾驶高度契合。在CARLA模拟器中进行的广泛实验结果表明了良好的效果，在适应性和效率方面均优于传统方法，从而推进了主动推理在现实世界自动驾驶应用中的潜力。