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Survey details deep multi-task learning for autonomous vehicles

作者 PulseAugur 编辑部 · [1 个来源] ·

本文全面回顾了应用于网联自动驾驶汽车（CAVs）的深度多任务学习（MTL）技术。文章探讨了MTL如何使单个模型能够处理感知、预测、规划和控制等多种任务，这对于在复杂驾驶场景中实现高效实时运行至关重要。该调查根据任务是仅由本车执行还是通过车联网（V2X）通信增强来对现有研究进行分类，并考察了V2X通信和无线资源管理背景下的MTL。作者们指出了当前研究的空白，并为推进CAV系统中的MTL提出了未来方向。 AI

影响 为自动驾驶系统中的多任务学习应用提供了结构化概述。

排序理由 关于应用于某一领域的特定AI技术的学术调查论文。

在 arXiv cs.LG 阅读 →

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Survey details deep multi-task learning for autonomous vehicles

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关于应用于某一领域的特定AI技术的学术调查论文。
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang ·

    面向车联网的深度多任务学习研究综述

    arXiv:2508.00917v2 Announce Type: replace-cross Abstract: 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 vehi…

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