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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器人技术 机器人技术 机器人技术
    • 人工智能的人工智能

    背景情况:

    • 自主系统需要准确的轨迹预测,以确保安全导航.
    • 人类行为本质上是不确定的,导致多种可能的未来道路.
    • 现有的方法往往难以捕捉未来轨迹的多样性.

    研究的目的:

    • 提供第一个关于多未来轨迹预测 (MTP) 的综合调查.
    • 引入新的分类系统来分类MTP框架.
    • 分析现有的MTP数据集,评估指标和模型.

    主要方法:

    • 对MTP方法进行系统审查和分类.
    • 在基准数据集上对最先进的模型进行比较分析.
    • 在ForkingPath数据集上的实验评估.

    主要成果:

    • 为MTP框架建立了独特的分类学.
    • 在各种MTP模型和数据集中比较和分析性能.
    • 确定了当前MTP研究中的关键挑战和局限性.

    结论:

    • 在自主系统中,MTP对于实现多样化,可接受和可解释的预测至关重要.
    • 该调查为未来对MTP和相关任务的研究提供了基础.
    • 未来的方向包括开发新的MTP系统和解决各种学习任务.