一个驾驶偏好意识框架用于车道变化预测
Ying Lyu1,2, Yulin Wang1,2, Huan Liu1,2
1Key Laboratory of Advanced Vehicle Integration and Control, Changchun 130011, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
预测人类驾驶车辆的车道变化对于自动驾驶安全至关重要. 这项研究开发了一个有效捕捉驾驶员偏好的模型,显著提高了混合交通中车道更改预测的准确性.
科学领域:
- 智能运输系统 智能运输系统
- 汽车工程中的人工智能
- 机器学习用于自动驾驶.
背景情况:
- 混合交通环境中,自动驾驶和人驾驶车辆带来了重大的安全挑战.
- 准确预测人类驾驶汽车的车道变化行为对于自动驾驶汽车导航至关重要.
- 司机的偏好带来了不确定性,使变化预测变得复杂.
研究的目的:
- 开发一种强大的方法来挖掘驾驶偏好和预测车道更改行为.
- 提高自动驾驶汽车的安全性和类似人类的决策.
- 整合多个来源的信息,包括车辆动态和驾驶员偏好,以改善预测.
主要方法:
- 使用车辆动态和统计特征构建了驾驶操作表示.
- 设计了一个基于SimCLR的对比学习框架,用于无监督地提取高维的驾驶偏好特征.
- 提出了一种双分支车道更改预测模型,将车辆时间状态特征与隐性驾驶偏好特征融合在一起.
主要成果:
- 提议的偏好功能提取器有效地区分了侵略性,正常和保守的驾驶风格.
- 与变压器和LSTM模型相比,双分支预测模型实现了明显更高的车道更改预测准确度.
- 使用HighD数据集进行实验验证.
结论:
- 开发的方法有效地捕捉和利用驾驶偏好来准确预测车道变化.
- 这项研究为提高自动驾驶系统的安全性和人与人相似性提供了技术基础.
- 这些发现有助于在混合自动驾驶和人驾驶交通场景中更可靠的决策.
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