标题： 连接自动驾驶车辆中的深度多任务学习综述 显示英文标题

标题： A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles

摘要： 联网自动驾驶车辆（CAVs）必须同时执行多个任务，例如目标检测、语义分割、深度估计、轨迹预测、运动预测和行为预测，以确保在复杂环境中的安全可靠导航。 车对一切（V2X）通信使得CAVs之间能够进行协作驾驶，从而缓解单个传感器的局限性，减少遮挡，并提高远距离的感知能力。 传统上，这些任务是通过不同的模型来处理的，这导致了高部署成本、计算开销增加以及实现实时性能的挑战。 多任务学习（MTL）最近作为一种有前景的解决方案出现，它能够在单一统一模型中实现多个任务的联合学习。 这提供了更高的效率和资源利用率。 据我们所知，本次综述是首个专注于CAVs背景下MTL的全面回顾。 我们首先对CAVs和MTL进行概述，以提供基础背景。 然后，我们探讨MTL在关键功能模块中的应用，包括感知、预测、规划、控制和多智能体协作。 最后，我们讨论现有方法的优点和局限性，识别关键的研究空白，并为未来研究提供方向，以推动CAVs系统中MTL方法的发展。 显示英文摘要

摘要： Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as object detection, semantic segmentation, depth estimation, trajectory prediction, motion prediction, and behaviour prediction, to ensure safe and reliable navigation in complex environments. Vehicle-to-everything (V2X) communication enables cooperative driving among CAVs, thereby mitigating the limitations of individual sensors, reducing occlusions, and improving perception over long distances. Traditionally, these tasks are addressed using distinct 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 single unified model. This offers improved efficiency and resource utilization. To the best of our knowledge, this survey is the first comprehensive review focused on MTL in the context of CAVs. We begin with an overview of CAVs and MTL to provide foundational background. We then explore the application of MTL across key functional modules, including perception, prediction, planning, control, and multi-agent collaboration. Finally, we discuss the strengths and limitations of existing methods, identify key research gaps, and provide directions for future research aimed at advancing MTL methodologies for CAV systems.