考虑到对面的交通,识别改变车道的意图:深度学习的进步揭示了新的见解
Hao Liu1, Tao Wang1, Wenyong Li1
1Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Accident; analysis and prevention
|February 7, 2024
概括
这项研究改进了在双车道道路上改车道的意图识别,使用变压器模型和新的眼睛跟踪算法. 它达到92.6%的准确性,几乎提前5秒就能识别车道的变化.
科学领域:
- * 智能交通运输系统
- * 驾驶员行为分析
- * 机器学习 * 机器学习
背景情况:
- * 改变车道 (LC) 意图识别模型在双车道,双向道路上的实际应用有限.
- * 需要先进的模型来预测驾驶员在复杂的道路环境中的意图.
- *现有的模型往往没有考虑与所有道路使用者的互动.
研究的目的:
- * 开发用于双车道,双向道路环境的高保真模拟.
- *使用变压器架构创建一个准确的LC意图识别模型.
- * 建议和验证一个新的标签算法 (VEL) 用于LC意图.
主要方法:
- * 构建一个高保真模拟的双车道,双向道路环境.
- * 开发基于变压器的模型来识别LC的意图.
- * 与时间窗口标签 (TWL) 相比,实施了一种新的车辆动力学和眼球跟踪 (VEL) 标签算法.
- * 在数据集中包括相对车辆的特征.
- * 应用LIME用于模型可解释性.
主要成果:
- * 变压器模型实现了最先进的性能,在使用VEL标签时提前4.59秒识别LC,准确度为92.6%.
- * VEL标签方法的性能优于传统的TWL.
- *包括相对的车辆数据,显著提高了LC识别精度.
- * LIME 分析显示,模型的重点是眼睛跟踪和交通互动.
结论:
- * 拟议的带有VEL标签的变压器模型显著提高了在双车道,双向道路上的LC意图识别.
- * 纳入驾驶员的目光和与周围车辆的互动对于改善预测至关重要.
- * 增强的预测能力提高了安全性和碰撞预警系统的时间.
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