基于车辆对车辆 (V2V) 和云计算通信的交叉碰撞预测和预防
Min Zeng1,2, Mohd Sani Mohamad Hashim1, Mohd Nasir Ayob3
1Mechanical Department, Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Arau, Perlis, Malaysia.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种智能交通碰撞预测模型,使用车辆对车辆通信和图形注意力网络. 该系统准确地识别了碰撞风险,提高了交通安全和效率.
科学领域:
- 智能运输系统 智能运输系统
- 交通安全工程 交通安全工程
- 机器学习用于运输.
背景情况:
- 越来越复杂的车辆需要先进的交通安全管理.
- 有效的碰撞风险评估对于道路效率和安全至关重要.
- 当前的系统在预测复杂的交通场景方面面临着挑战.
研究的目的:
- 开发一个智能交通碰撞预测模型.
- 为了利用车辆对车辆 (V2V) 通信和图表注意力网络 (GAT) 进行风险评估.
- 提高智能运输系统的决策能力.
主要方法:
- 使用V2V通信收集车辆数据 (轨迹,速度,加速度,相对位置).
- 构建交通环境的图形表示.
- 应用GAT来提取车辆间的交互特征.
- 使用深度强化学习 (DRL) 优化驾驶策略.
主要成果:
- 拟议的框架实现了超过80%的碰撞识别精度 (真实预警率).
- 分析表明该模型在公共和现实世界数据集上的有效性和稳定性.
- 错误检测指标得到了彻底的评估.
结论:
- 这种新的方法显著提高了智能运输系统中的交通安全.
- 该模型提高了交通管理决策效率.
- 这项研究为碰撞预测提供了强大的技术进步.
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