基于深度学习的预测模型,用于自动驾驶汽车网络中的实时事故预防
Ahmed Almutairi1, Abdullah Faiz Al Asmari2, Fayez Alanazi3
1Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, 11952, Majmaah, Saudi Arabia. a.alaoni@mu.edu.sa.
Scientific reports
|July 2, 2025
概括
本研究介绍了一种自动驾驶汽车安全模型A-LAPPM,用于预测和预防道路交通事故. 该模型显著提高了预测准确性和响应时间,在复杂的驾驶场景中降低了事故发生率.
科学领域:
- 人工智能的人工智能
- 自动驾驶汽车技术自动驾驶汽车技术
- 道路安全工程 道路安全工程
背景情况:
- 越来越多的交通量给道路安全带来了重大挑战.
- 复杂的事故预测和预防技术至关重要.
- 自动驾驶汽车 (AV) 网络提供实时避免碰撞的能力.
研究的目的:
- 为自动驾驶汽车网络提供创新的事故预测和预防模型.
- 通过实时识别和响应潜在的事故危险,提高道路安全.
主要方法:
- 开发了基于注意力的长期和短期记忆自编码器 (A-LAPPM) 模型.
- 来自车辆传感器,车辆对车辆 (V2V) 通信和环境变量的集成数据.
- 利用长期和短期记忆 (LSTM) 单元进行顺序学习,并使用注意力机制来提高焦点.
主要成果:
- 实现了大约11.8%更高的预测准确度.
- 显示了28.5%更快的响应时间.
- 在复杂的驾驶场景中,减少了50%的事故率.
结论:
- A-LAPPM模型有效地预测和预防自动驾驶汽车网络中的事故.
- 该模型提高了整体自动驾驶汽车的性能和道路安全.
- 实验结果验证了模型的准确性,速度,效率和弹性.
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