基于深度学习和模糊逻辑的自动制动驾驶员辅助系统
A R García-Escalante1, R Q Fuentes-Aguilar2, A Palma-Zubia1
1Tecnológico de Monterrey, Escuela de Ingenieria y Ciencias, Zapopan, Jalisco, México.
PloS one
|December 31, 2024
概括
本研究介绍了1级高级驾驶辅助系统 (ADAS),用于使用交通信号灯检测的自动制动. 现实世界的测试验证了系统的有效性.
科学领域:
- 计算机视觉和机器人技术
- 智能运输系统 智能运输系统
- 汽车工程 汽车工程
背景情况:
- 先进的驾驶辅助系统 (ADAS) 对于实现完全的运输自动化至关重要.
- 目前的ADAS研究通常依赖于室内实验,需要现实世界的验证才能实际实施.
- 交通灯检测和自动制动是提高车辆安全和自主性的关键组成部分.
研究的目的:
- 提出并验证一级ADAS,重点是使用交通信号灯检测的自动制动.
- 为了解决对ADAS功能户外,现实世界的测试需求.
- 开发一个精确的同步和短的反应时间制动决策的系统.
主要方法:
- 实现了一个NVIDIA Jetson TX2与ZED立体相机用于交通信号灯检测.
- 利用模糊的推断系统,根据交通信号灯状态和距离做出决策.
- 开发使用EfficientDet D0.0的一级交通信号灯状态探测器.
- 通过道路实验进行验证.
主要成果:
- EfficientDet D0模型在不同距离的交通信号检测 (0.96在<13m,0.89在15m) 中获得了高的mAP分数.
- 模糊逻辑制动配置文件显示,距离的平均RMSE为0.9m,制动力的平均RMSE为0.05m.
- 集成系统的快速响应时间为0.23秒.
结论:
- 开发的1级ADAS有效地集成了交通信号灯检测和自动制动,以适应现实环境.
- 该系统在道路实验中的性能验证了其在提高汽车安全方面的潜力.
- 这项研究通过实践验证来推进自动驾驶系统的最新技术.
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