基于CNN-BiLSTM的高速公路系统交叉连续分离部分的驾驶行为识别
Guanghui Sun1, Hongbin Zhang2, Liande Zhong3
1School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, 255000, China.
Scientific reports
|March 28, 2025
概括
研究人员开发了一种驾驶行为谱 (DBS) 来分析复杂的高速公路交叉点. 该系统准确地识别驾驶行为及其转变,为更安全的道路设计提供了洞察力.
科学领域:
- 交通工程是交通工程.
- 交通运输中的人工智能
- 驾驶过程中的人类因素
背景情况:
- 高速公路交叉路口带来复杂的驾驶环境,存在重大安全风险.
- 了解驾驶行为和转换对于减轻这些风险至关重要.
研究的目的:
- 在连续分离的高速公路路段调查驾驶行为和过渡模式.
- 开发一种用于分析和分类复杂交通场景中的驾驶行为的新方法.
主要方法:
- 进行了模拟驾驶实验,以收集驾驶行为数据.
- 一个驾驶行为频谱 (DBS) 为连续分离的部分构建.
- 使用CNN-BiLSTM模型来识别特定的驾驶行为 (直行,换车道,减速,转).
- 一个隐藏的马尔科夫模型 (HMM) 量化了驾驶状态之间的过渡.
主要成果:
- DBS有效地捕捉了驾驶行为的时间变化.
- 该CNN-BiLSTM模型在识别四种典型的驾驶行为方面实现了98%的平均准确性.
- 观察到车道变化发生在分离点 (121m和78m) 之前的特定距离.
- 在HMM成功阐明了驾驶状态过渡模式.
结论:
- 开发的模型为分析复杂高速公路环境中的驾驶行为提供了强大的框架.
- 这些发现为识别危险区域和优化交换机设计以提高安全性提供了有价值的数据.
- 这项研究有助于智能交通系统和道路安全工程的发展.
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