研究基于机器学习的行人交叉路口决策模型和预测
Jun Cai1, Mengjia Wang1, Yishuang Wu1
1School of Architecture & Fine Art, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
人工智能 (AI) 通过改善智能城市的行人交叉预测来提高道路安全. 机器学习,特别是支持矢量机 (SVM),准确地预测智能交通系统的行人行为.
科学领域:
- 智能运输系统 智能运输系统
- 智慧城市技术 智慧城市技术
- 在交通管理中的人工智能.
背景情况:
- 在智慧城市中,道路交通安全至关重要,需要先进的解决方案.
- 对于智能交通系统来说,行人交叉行为分析至关重要.
- 传统的模型与行人过道动态的复杂性作斗争.
研究的目的:
- 使用机器学习 (ML) 来优化行人交叉预测.
- 为了提高判断和模拟行人违规行为的准确性.
- 应用ML模型以改善交通模拟和安全.
主要方法:
- 利用OpenCV进行图像识别来分析行人行为.
- 训练并测试多个ML模型:决策树,多层感知子,贝叶斯算法和支持矢量机器 (SVM).
- 从中国城市的信号交叉路口提取出真实的行人交叉数据.
主要成果:
- 支持矢量机 (SVM) 模型表现出卓越的准确性.
- SVM有效地预测了行人穿越的概率和速度.
- 确定SVM为最优的行人穿越预测和交通模拟.
结论:
- 机器学习显著提高了行人穿越预测.
- SVM是智能运输安全应用程序的高效模型.
- 这项研究有助于通过人工智能实现更安全,更有效的城市交通.
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