基于局部线性波形神经网络的无气味卡尔曼过器用于车辆碰撞预估警报系统,并确保稳定的车辆与基础设施通信
Yonas Kebede Lema1, Satyasis Mishra2, Demissie J Gelmecha1
1Department of ECE, Adama Science and Technology University, Adama, Ethiopia.
Applied bionics and biomechanics
|December 30, 2024
概括
这项研究引入了一种使用局部线性波纹神经网络 (LLWNN) 和无气味卡尔曼波器 (UKF) 的新型车辆碰撞预警系统. 集成系统通过结合传感器数据和车辆与一切 (V2X) 通信来提高驾驶员的安全性,以准确预测碰撞.
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
- 传感器融合和信号处理
背景情况:
- 全球事故死亡率的上升凸显了对先进驾驶辅助系统 (ADAS) 的需求.
- 环境因素和传感器限制 (视线,视野) 降低了当前ADASs的准确性.
- 现有的系统难以检测传感器范围之外的风险或受阻时,导致潜在的危险报告不足.
研究的目的:
- 开发一个先进的车辆碰撞估计和预警系统.
- 提高车辆状态估计和碰撞预测的准确性和可靠性.
- 通过改善除了车载传感器之外的情境意识来减轻交通事故和减少死亡人数.
主要方法:
- 集成局部线性波形神经网络 (LLWNN) 用于车辆状态预测.
- 使用无气味的卡尔曼波器 (UKF) 进行最佳的数据融合.
- 利用车辆到一切 (V2X) 通信来增强情境意识和实时数据集成.
主要成果:
- 该LLWNN模块准确预测未来的车辆状态和潜在的碰撞风险.
- UKF有效地将LLWNN预测与实时传感器和V2X数据融合在一起,以精确估计车辆状态.
- 该系统提供及时的碰撞警报,即使是直接传感器视图之外的威胁.
结论:
- 集成的LLWNN-UKF系统显著提高了碰撞预测的准确性和可靠性.
- 将传感器数据与V2X通信相结合,可以克服单个传感器的局限性.
- 这种方法有望大幅减少因司机疏忽造成的交通事故,伤害和死亡事故.
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