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在线交通事故风险推断方法使用检测变压器和支持向量机,通过仿生算法进行优化
Bihui Zhang1, Zhuqi Li2, Bingjie Li3
1School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China.
Biomimetics (Basel, Switzerland)
|November 26, 2024
概括
本研究引入了一种新的在线方法,用于通过先进的对象检测和优化算法来估计城市交通事故风险. 该方法提高了自主系统和实时监控的道路安全预测.
科学领域:
- 交通安全工程 交通安全工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 尽管有安全干预措施,估计城市交通事故风险仍然具有挑战性.
- 现有的方法在实时预测和全面的风险评估方面扎.
- 自主系统需要可靠的道路状况和风险预测.
研究的目的:
- 提出城市交通事故风险的在线推断方法.
- 为自动驾驶汽车和机器人开发强大的数据推断能力.
- 增强实时道路状况预测和风险监测.
主要方法:
- 从城市交通数据和事故新闻创建了TAR-1数据集.
- 开发了基于上下文引导的重建功能基于网络的城市交通物体检测模型 (TAR-DETR).
- 提出了一种混合优化算法 (WOA-SA-SVM),将鱼优化算法和模拟回火用于SVM参数调整.
主要成果:
- 在城市交通物体检测方面,TAR-DETR实现了76.8%的准确性,超过了最先进的模型.
- TAR-2数据集由六个风险特征和三个类别创建.
- 在城市交通事故风险推断中,WOA-SA-SVM方法实现了80%的平均准确性.
结论:
- 拟议的在线推断方法有效地估计了城市交通事故风险.
- TAR-DETR和WOA-SA-SVM方法为自主系统提供了强大的功能.
- 这种方法可以实现实时预测,持续监控和及时的道路辅助.
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