基于光纤分布式声学传感器的交通流量与速度监测
Traffic Flow and Speed Monitoring Based On Optical Fiber Distributed Acoustic Sensor
AI总结：
针对传统交通监测设备在恶劣天气和复杂场景下的局限性，提出基于光纤分布式声学传感器（DAS）的交通流量监测方法，通过创新车辆轨迹提取算法实现更准确的轨迹提取与流量计算，为交通监测提供新型可靠手段。
摘要：
在智能交通系统领域，准确可靠的交通监测至关重要。摄像头、激光雷达等传统设备在恶劣天气和复杂交通场景下存在局限性，促使人们需要更具韧性的技术。本文提出一种基于光纤分布式声学传感器（DAS）的交通流量监测方法，设计了创新的车辆轨迹提取算法以推导交通流量统计数据。在光纤瀑布图处理中，采用巴特沃斯低通滤波器和峰值位置搜索算法确定车辆进入位置，随后提出逐行匹配算法有效跟踪轨迹。实验在高速公路、隧道和城市场景中开展，可视化结果显示，该方法不仅比基于霍夫变换、拉东变换的经典方法及MUSIC波束形成算法更准确地提取车辆轨迹，还能利用低成本声学传感器计算交通流量信息，为交通流量监测提供了一种可与现有视觉方法集成的新型可靠手段。
英文摘要：
In the realm of intelligent transportation systems, accurate and reliable traffic monitoring is crucial. Traditional devices, such as cameras and lidars, face limitations in adverse weather conditions and complex traffic scenarios, prompting the need for more resilient technologies. Thispaperpresentstrafficflowmonitoringmethodusingopticalfiber-baseddistributedacoustic sensors(DAS).Aninnovativevehicletrajectoryextractionalgorithmisproposedtoderivetraffic flow statistics. In the processing of optical fiber waterfall diagrams, Butterworth low-pass filter and peaks location search algorithm are employed to determine the entry position of vehicles. Subsequently, line-by-line matching algorithm is proposed to effectively track the trajectories. Experiments were conducted in highway, tunnel and city scenarios. Visualizations show that ourapproachnotonlyextractsvehicletrajectoriesmoreaccuratelythantheclassicalHoughand Radon transform-based methods and MUSIC beamforming algorithm, but also facilitates the calculation of traffic flow information using the low-cost acoustic sensors. It provides a new reliable means for traffic flow monitoring which can be integrated with existing methods like vision-based method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
