启用CVIS的交叉路口的积极安全:基于高准确度轨迹重建和动态风险评估的框架
Yunxuan Li1, Shihao Wang2, Lishengsa Yue3
1Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China.
Accident; analysis and prevention
|January 21, 2026
概括
本研究引入了一个边缘计算框架,用于准确地重建车辆轨迹和在十字路口进行风险评估. 这种新的方法通过提高冲突检测准确性和减少合作车辆基础设施系统的延迟来提高主动安全性.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 传统的轨迹重建方法在十字路口与复杂的车辆动态作斗争.
- 对于实时边缘部署而言,深度学习模型在计算上昂贵.
- 准确的轨迹数据对于主动交叉路口安全至关重要.
研究的目的:
- 开发一个高效的,边缘计算增强的框架,用于高准确度的车辆轨迹重建.
- 改进动态风险评估和十字路口冲突检测.
- 在合作车辆基础设施系统 (CVIS) 中实现实时主动安全干预.
主要方法:
- 一个两阶段的框架,结合了基于物理学的约束,自适应波形变换和混合值来进行轨迹重建.
- 一个基于车辆轮的冲突算法 (VOCA) 用于使用空间重叠分析进行敏感,及时的冲突检测.
- 在NVIDIA Jetson边缘设备上实现和验证,用于现实世界的性能评估.
主要成果:
- 通过有效的噪声抑制,加速波动减少了98.66%.
- VOCA检测到的冲突比传统的中心点方法多了77.47%.
- 实现实时性能,处理延迟低于每车辆每100毫秒.
结论:
- 拟议的框架为准确,低延迟的轨迹重建和冲突警告提供了有效的解决方案.
- 边缘计算的增强使CVIS的实用实时应用成为了积极的交叉路口安全.
- 基于轮的冲突检测在复杂的城市环境中明显优于基于点的方法.
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