Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments. Moreover, through vehicle-to-everything (V2X) communication, cooperative perception and driving among CAVs can be enabled, thereby mitigating the limitations of individual vehicles, while it also introduces stringent latency, reliability, and bandwidth constraints. Traditionally, tasks are addressed using separate models, which leads to high deployment costs, increased computational overhead, and challenges in achieving real-time performance. Multi-task learning (MTL) has recently emerged as a promising solution that enables the joint learning of multiple tasks within a unified model. This offers improved efficiency and resource utilization. To the best of our knowledge, this survey is the first comprehensive review focusing on deep MTL in CAVs. We begin with an overview of CAVs and MTL to provide foundational background. Then, we review MTL approaches across key functional domains in CAVs, including perception, prediction, planning, control, as well as V2X communications and radio resource management (RRM). For the first four domains, we categorize existing works under ego vehicle-only (onboard-only) and V2X-enhanced cooperative (multi-agent) paradigms. We further discuss V2X communications and RRM as communication-centric MTL problems. Finally, we discuss the strengths and limitations of existing methods, identify key research gaps, and provide future research directions aimed at advancing MTL methodologies for CAV systems.

翻译：互联自动驾驶汽车需同时执行多种任务，如感知、预测、规划与控制，以确保在复杂环境中安全可靠地导航。通过车联万物通信，车辆间的协同感知与驾驶得以实现，从而克服单车感知的局限性，但这同时也引入了严格的时延、可靠性与带宽约束。传统上，各项任务通过独立模型处理，导致部署成本高、计算开销大，且难以满足实时性需求。多任务学习近期成为一种有前景的解决方案，它能在统一模型中联合学习多项任务，从而提升效率与资源利用率。据我们所知，本文是首个聚焦于自动驾驶汽车中深度多任务学习的全面综述。我们首先概述自动驾驶汽车与多任务学习的基础背景，随后梳理自动驾驶汽车关键功能域中的多任务学习方法，涵盖感知、预测、规划、控制，以及车联万物通信与无线电资源管理。针对前四个功能域，我们分别从单车车载范式与车联万物增强协同范式两个角度对现有工作进行分类。此外，我们将车联万物通信与无线电资源管理作为以通信为中心的多任务学习问题展开讨论。最后，我们分析了现有方法的优势与局限性，识别关键研究空白，并提出了推动自动驾驶系统多任务学习方法发展的未来研究方向。