具有多种感知能力的深度学习辅助传感器,用于智能驾驶员辅助监控系统
Jingliang Lv1, Yu Wang1, Haiyue Fu1
1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150042, China.
ACS applied materials & interfaces
|March 6, 2024
概括
这项研究介绍了使用原木传感器和AI的智能驾驶辅助监控系统 (IDAMS). 该IDAMS增强了驾驶员的控制和隐私,在识别驾驶员和监控行为方面达到99.38%的准确性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 当前的驾驶辅助系统 (DAS) 提供了控制优势,但受到复杂的结构和隐私侵犯的影响.
- 这些局限性阻碍了驾驶员辅助技术的发展和采用.
研究的目的:
- 提出一个智能驾驶辅助监控系统 (IDAMS),克服现有系统的局限性.
- 为了提高车辆的控制,减少驾驶员的疲劳,并确保驾驶员的隐私.
主要方法:
- 开发基于克雷斯林原形结构的 triboelectric 传感器 (KOS-TS) 来捕获驾驶行为信号.
- 实施一个多尺度卷积神经网络 (CNN) 来分析传感器数据,包括时间信息.
- 利用压力,时间和传感器触发序列等功能进行分析.
主要成果:
- 拟议的IDAMS在驾驶员识别准确度方面取得了显著的改进,在八个不同的用户中从96.25%增加到99.38%.
- 证明成功监测驾驶行为和准确区分不同的行为.
- 验证了卓越的无人机检测 (HOD) 能力.
结论:
- IDAMS为驾驶员辅助提供了一种保护隐私和有效的解决方案.
- 该系统显示出在安全警告,个性化驾驶体验和车辆内人机交互方面的应用有很大的潜力.
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