基于卷积神经网络和转移学习在异常天气下识别激进的驾驶行为
Ziyu Zhang1,2, Shuyan Chen1,2, Hong Yao1,2
1School of Transportation, Southeast University, Nanjing, China.
Traffic injury prevention
|July 24, 2024
概括
转移学习提高了在异常天气下积极驾驶的识别能力. 在正常条件下训练的模型在雾和雨中达到0.81准确度,优于未转移的模型.
科学领域:
- 交通安全和人工智能.
- 机器学习在运输中的应用.
背景情况:
- 积极驾驶会增加碰撞的风险,尤其是在异常天气期间.
- 在恶劣天气下收集足够的驾驶数据是具有挑战性的.
研究的目的:
- 为各种气候条件开发强大的积极驾驶识别模型.
- 用转移学习解决异常天气场景中的数据稀缺问题.
主要方法:
- 使用驾驶模拟器在正常和异常天气下收集数据.
- 采用K-means集群来对驾驶行为进行分类 (侵略性,正常,谨慎).
- 在正常天气数据上训练了一个卷积神经网络 (CNN) 模型,并通过转移学习对雾和雨天进行微调.
主要成果:
- 转移的CNN模型在雾和雨天条件下达到0.81准确度.
- 这显著优于非转移模型 (0.72在雾中,0.69在雨中).
结论:
- 转移学习为识别有限数据的攻击性驾驶提供了重要的价值.
- 这种方法对于在恶劣天气条件下解决驾驶行为识别挑战是可行的.
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