提取驾驶行为原体,考虑到驾驶员的期望和车辆动态
Yuanyuan Ren1, Xiaotong Cui2, Xuelian Zheng1
1Transportation College, Jilin University, Changchun, Jilin, China.
PloS one
|December 31, 2025
概括
这项研究引入了一个新的框架,用于提取驾驶行为原始体. 该方法精确地对驾驶行为进行细分,并将其集成为先进的驾驶辅助系统和自动驾驶汽车的有意义模式.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 对驾驶行为的细粒度分析对于开发先进的驾驶辅助系统 (ADAS) 和自动驾驶汽车 (AV) 是必不可少的.
- 现有的方法往往难以捕捉人类驾驶模式的层次和细微性质.
研究的目的:
- 开发一种用于提取等级驾驶行为原始性的新框架.
- 为了提高驾驶行为细分和集群的准确性和可解释性.
主要方法:
- 一个两阶段的框架,结合了基于贝叶斯模型的聚合序列分割 (BMASS) 用于行为细分和基于可变合的潜伏迪里克莱特分配 (VC-LDA) 用于原始集群.
- 构建一个包含车辆运动状态和驾驶员期望的多类型功能空间.
- 在VC-LDA中开发特征合感知离散化过程,以处理非线性特征合和时间异步.
主要成果:
- 拟议的BMASS模型实现了精确的行为细分,以细分持续时间和定位为关键质量指标.
- 在聚类方面,VC-LDA方法显著优于GMM-LDA,表现出较低的复杂性和更高的类内紧性.
- 该框架提供了驾驶状态的增强物理解释性.
结论:
- 这种新的框架提供了一种自动化和高效的方法来理解和建模驾驶员的行为.
- 这种方法为开发更安全,更复杂的ADAS和AVs提供了有价值的见解.
- 在集群驾驶状态方面,VC-LDA方法是显著的进步.
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