Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors

By Junxuan Zhao, Hao Xu, Hongchao Liu, Jianqing Wu, Yichen Zheng, Dayong Wu. Transportation Research Part C: Emerging Technologies, 2019

326 citations recorded by the source corpus.

Abstract Light Detection and Ranging (LiDAR) is a remote sensing technology widely used in many areas ranging from making precise medical equipment to creating accurate elevation maps of farmlands. In transportation, although it has been used to assist some design and planning works, the application has been predominantly focused on autonomous vehicles, regardless of its great potential in precise detection and tracking of all road users if implemented in the field. This paper explores fundamental concepts, solution algorithms, and application guidance associated with using infrastructure-based LiDAR sensors to accurately detect and track pedestrians and vehicles at intersections. Based on LiDAR data collected in the field, investigations were conducted in the order of background filtering, object clustering, pedestrian and vehicle classification, and tracking. The results of the analysis include accurate and real-time information of the presence, position, velocity, and direction of pedestrians and vehicles. By studying the data from infrastructure-mounted LiDAR sensors at intersections, this paper offers insights into some critical techniques that are valuable to both researchers and practitioners toward field implementation of LiDAR sensors.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/7cf2156c52377f566b1892bcf13c5d62ad80f549

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Yu Wu
2. Qingqing Niu
3. Zhangjing Wang

In Computer Vision and Pattern Recognition

1. Yu Wu
2. Qingqing Niu
3. Hanyu Li
4. Zaiqing Nie
5. Zhangjing Wang

Citations in the corpus

• DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection (2022, background): “Works of V2I [6,31] leverage infrastructure LiDAR data to generate and broadcast detection results.”
• Multi-Sensor Fusion in Automated Driving: A Survey (2020, methodology): “In reference [70], a 16-line LiDAR is implemented to percept the position, velocity, and direction of pedestrians and vehicles in the streetscape.”