一种认知数字双胞胎方法,以改善驾驶员的合规性和事故预防
Yi Gu1, Shuhang Li1, Ji Qi1
1School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
Accident; analysis and prevention
|January 8, 2025
概括
这项研究介绍了一种基于认知数字双胞胎的驾驶辅助系统 (CDAS),通过模拟个人的行为来个性化驾驶员支持. CDAS提高了驾驶安全和用户接受度,超出了传统的高级驾驶辅助系统 (ADAS).
科学领域:
- 人与计算机的交互
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 传统的先进驾驶员辅助系统 (ADAS) 缺乏个性化,往往无法考虑个体驾驶员的行为和决策过程.
- 这种不符合ADAS的情况可能会限制它们在提高驾驶安全方面的有效性.
- 现有的数据驱动方法在没有广泛的注释数据集的情况下,难以利用驾驶员观察行动.
研究的目的:
- 推出一种新的基于认知数字双胞胎的驾驶辅助系统 (CDAS),以个性化驾驶辅助.
- 开发一个能够动态适应个体驾驶员行为和决策过程的系统.
- 提高驾驶辅助系统的安全性,必要性和用户接受度.
主要方法:
- 开发一种认知数字双胞胎 (CDT) 框架,包含个性化的驾驶决策模型.
- 基于实时驾驶员控制和观察操作的模型的动态更新.
- 实验验证将CDAS与传统的ADAS和深度学习模型进行比较.
主要成果:
- 在实验验证中,CDT与实际的驾驶员观察行为密切一致.
- 通过结合驾驶员观察行动,CDAS实现了优异的车道变化预测,超过了深度学习分类器.
- 与传统的ADAS相比,CDAS显著降低了风险,并提高了用户接受度.
结论:
- CDAS代表了个性化和适应性驾驶辅助的重大进步.
- 认知数字双胞胎方法有效地模拟个人驾驶员的行为,以提高安全性.
- 这种个性化的系统显示出未来汽车安全应用的巨大潜力.
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