通过在交通流基本图下进行对抗性混来提高交通事故风险评估模型的稳定性
Rongjie Yu1, Lei Han2, Mohamed Abdel-Aty2
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 4800 Cao'an Road, 201804 Shanghai, China.
Accident; analysis and prevention
|October 28, 2023
概括
对于碰撞风险的深度学习模型与现实世界的交通波动作斗争. 一种新的对抗性训练方法显著提高了它们对抗这些不可预测变化的强度.
科学领域:
- 交通安全 交通安全 交通安全
- 人工智能的人工智能是人工智能.
- 深度学习应用程序深度学习应用程序
背景情况:
- 深度学习 (DL) 模型增强了碰撞风险评估,但在现实世界的交通动态中面临着稳定性挑战.
- 对输入干扰的敏感性可能会导致基于DL的交通安全模型中的预测不准确.
研究的目的:
- 解决基于DL的撞车风险评估模型中的关键稳定性问题.
- 调查和提出对策,以提高模型的稳定性,以应对交通波动.
主要方法:
- 通过混合交通流基本图下的碰撞和非碰撞样本来生成流量对抗示例 (TF-AE).
- 开发了一种以覆盖面为导向的对抗性培训方法,以提高不平衡数据集和各种崩风险模式的稳定性.
主要成果:
- 基线卷积神经网络 (CNN) 模型显示,当暴露于TF-AEs时,准确度下降了8%,灵敏度下降了18%.
- 提出的对抗训练方法防止了76.5%的准确性下降和98.9%的对TF-AEs的敏感性下降.
结论:
- 提出的以覆盖面为导向的对抗性培训显著提高了DL模型对碰撞风险评估的稳定性.
- 该方法有效地减轻了由真实世界的交通波动和数据不平衡引起的性能下降.
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