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基于视觉检测和跟踪管道的感知错误对自动驾驶系统中行人轨迹预测的影响
Wen-Hui Chen1, Jiann-Cherng Wu1, Yury Davydov1
1Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
在行人检测和跟踪方面的错误会对自动驾驶安全产生重大影响. 这项研究量化了这些上游错误如何在现实场景中影响轨迹预测的准确性.
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:
- 预测行人轨迹对于自动驾驶汽车避免碰撞至关重要.
- 传统方法依赖于准确的行人检测和跟踪,但研究中经常忽视现实世界的不准确性.
- 现有的轨迹预测模型通常在公共数据集上进行评估,忽略了上游过程中的累积错误.
研究的目的:
- 调查来自集成检测,跟踪和轨迹预测管道错误的累积影响.
- 实证地分析在管道的每个阶段引入的错误及其对预测准确性的集体影响.
- 通过在台湾收集的定制数据集来评估这些综合模型.
主要方法:
- 开发了一个集成的管道,结合了行人检测,跟踪和轨迹预测.
- 进行经验分析以量化每个阶段 (检测,跟踪,预测) 的错误.
- 在捕捉真实世界条件的自定义数据集上评估模型性能.
主要成果:
- 检测和跟踪错误显著影响下游轨迹预测的准确性.
- 经验证明了整个管道错误的累积效应.
- 在各种自定义数据集中评估了性能,突出了实际影响.
结论:
- 现实世界的检测和跟踪不准确性是影响自动驾驶安全系统的关键因素.
- 未来的研究必须考虑端到端的管道性能,而不仅仅是孤立的预测模型.
- 解决上游错误传播对于稳健的行人轨迹预测至关重要.
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