Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system. Machine learning-based methods are widely used in CAVs for crucial tasks like perception, motion planning, and motion control, where machine learning models in CAVs are solely trained using the local vehicle data, and the performance is not certain when exposed to new environments or unseen conditions. Federated learning (FL) is an effective solution for CAVs that enables a collaborative model development with multiple vehicles in a distributed learning framework. FL enables CAVs to learn from a wide range of driving environments and improve their overall performance while ensuring the privacy and security of local vehicle data. In this paper, we review the progress accomplished by researchers in applying FL to CAVs. A broader view of the various data modalities and algorithms that have been implemented on CAVs is provided. Specific applications of FL are reviewed in detail, and an analysis of the challenges and future scope of research are presented.

翻译：网联自动驾驶车辆是汽车领域的新兴技术之一，有望缓解事故、交通拥堵和污染物排放等问题，从而构建安全、高效、可持续的交通系统。基于机器学习的方法被广泛应用于网联自动驾驶车辆的感知、运动规划与运动控制等关键任务中，但现有模型仅依赖本地车辆数据进行训练，在面临新环境或未知场景时性能难以保证。联邦学习为网联自动驾驶车辆提供了有效解决方案，能够通过分布式学习框架实现多车协同模型开发。该方法使车辆能从广泛驾驶环境中学习，提升整体性能，同时保障本地车辆数据的隐私安全。本文系统梳理了研究人员将联邦学习应用于网联自动驾驶车辆的研究进展，全面介绍了已在网联自动驾驶车辆中实现的多模态数据与算法，详细综述了联邦学习的具体应用场景，并分析了当前面临的挑战与未来研究方向。