调查与道路交通事故相关的街景环境特征,使用街景图像和计算机视觉
1Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China.
Accident; analysis and prevention
|November 24, 2024
概括
街景特征对道路交通事故产生重大影响. 结合深度学习方法,如语义细分和对象检测,可以改善碰撞预测,为城市规划和交通安全提供见解.
科学领域:
- 城市规划和交通安全.
- 计算机视觉用于地理空间分析.
- 道路安全工程工程 道路安全工程
背景情况:
- 传统的道路安全评估依赖于低效的现场调查.
- 街景图像 (SVIs) 为数据收集提供了一个具有成本效益的替代方案.
- 现有的深度学习方法往往忽略了特征形状和轮.
研究的目的:
- 通过结合深度学习方法全面提取街景特征.
- 分析详细的街景特征与道路交通事故类型之间的关系.
- 为了提高道路交通事故预测模型的准确性.
主要方法:
- 使用百度街景图像 (SVI) 来进行特征提取.
- 用于像素级特征比例 (道路,建筑等) 的语义细分. ) 的情况.
- 对于离散的元素 (车辆,行人,交通信号灯) 应用物体检测.
- 使用零膨胀负二项式回归来模型崩数据.
主要成果:
- 集成的深度学习方法显著提高了碰撞预测的准确性.
- 车辆对车辆之间的碰撞和单车碰撞有很大的影响.
- 行人是车辆与行人碰撞的关键因素.
- 道路表面,人行道和植物与较高的撞车风险相关.
- 建筑物和树木与减少碰撞频率的相关性.
结论:
- 将语义细分和对象检测结合起来,可以提供更强大的街景分析.
- 环境特征在道路交通安全方面发挥着至关重要的作用.
- 结果为制定有效的城市规划和交通安全战略提供了可操作的见解.
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