MulCPred:学习多模式概念,以解释行人行动预测.
Yan Feng1, Alexander Carballo2,3,4, Keisuke Fujii1
1Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
MulCPred通过提供可解释的,多模式的基于概念的见解来增强行人行动预测. 这一框架提高了自动驾驶系统的可靠性和通用性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 预测行人动作对于自动驾驶安全至关重要.
- 目前的方法缺乏可靠预测所需的可解释性.
- 现有的基于概念的方法与多模式数据和输入细节作斗争.
研究的目的:
- 引入MulCPred,这是一个用于可解释行人行动预测的新框架.
- 在多式联运场景中解决以往基于概念的方法的局限性.
- 提高行动预测模型的可信度和概括能力.
主要方法:
- MulCPred使用线性聚合器来实现多模式概念集成和解释.
- 一个通道智能重新校准模块使注意力集中在当地时空细节上.
- 功能规范化损失促进了多样化的概念学习,以防止模式崩.
主要成果:
- MulCPred在行人行动预测中展示了更好的解释性.
- 该框架显示了有希望的结果,没有显著的性能退化.
- 删除未被识别的概念可以提高跨数据集预测性能和概括性.
结论:
- MulCPred为可解释的行人行动预测提供了一个有前途的解决方案.
- 该方法有效地处理多模式数据和本地输入细节.
- 该框架显示了在现实世界应用中改进通用化的潜力.
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