Traffic Volume Detection Using Infrastructure-Based LiDAR under Different Levels of Service Conditions

Zhao, Junxuan; Xu, Hao; Zhang, Yibin; Tian, Yuan; Liu, Hongchao

doi:10.1061/jtepbs.0000595

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Some other researchers advanced this area by integrating roadside LiDAR into traffic management. Zhao et al [19] researched lane and movement-based traffic volume data collection using infrastructure-based LiDAR under different congestion levels and traffic compositions, covering signalized intersections, pedestrian crossings, work zones, stopsign intersections, metered/unmetered ramps, and rural highways. Lv et al [20] proposed a LiDAR-enhanced connected infrastructure solution to collect traffic data of traffic participants using roadside LiDAR and broadcast the message through DSRC to enable connected vehicle application.…”

Section: Related Workmentioning

confidence: 99%

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Zhang

Jin

2022

Journal of Advanced Transportation

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In this study, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into the new data structure to store the information of range, azimuth, and intensity. Then, the dynamic mode decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The coarse-fine triangle algorithm (CFTA) automatically finds the dividing value to separate the moving targets from static background according to range information. After intensity and range background subtraction, the foreground moving objects will be detected using a density-based detector and encoded into the state-space model for tracking. The output of the proposed solution includes vehicle trajectories that can enable many mobility and safety applications. The method was validated at both path and point levels and outperformed the state of the art. In contrast to the previous methods that process directly on the scattered and discrete point clouds, the dynamic classification method can establish the less sophisticated linear relationship of the 3D measurement data, which captures the spatial-temporal structure that we often desire.

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Section: Related Workmentioning

confidence: 99%

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Zhang

Jin

2022

Journal of Advanced Transportation

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“…Furthermore, for object detection and tracking, a sufficient number of laser points or rings (circular traces of points) need to be reflected from the surface of an object. Prior exploration of LiDAR sensor quality has focused on general LiDAR sensing patterns against static objects ( 7 ) or noises caused by sensor quality and cleanness ( 8 ). This paper presents a comprehensive analytic and assessment model for the sensing blind zones of roadside LiDAR sensors in vehicle detection and tracking applications.…”

mentioning

confidence: 99%

Roadside LiDAR Sensor Configuration Assessment and Optimization Methods for Vehicle Detection and Tracking in Connected and Automated Vehicle Applications

Jin

Zhang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This paper develops an assessment and optimization model for configuring roadside LiDAR (Line Detection and Ranging) installation. More specifically, an analytic and a simulation model have been developed to analyze the detection blind zones and their impact on vehicle detection and tracking capabilities in Connected and Automated Vehicle (CAV) applications. The proposed model can derive the area and height of the detection blind zones from a given roadside LiDAR location and road geometry. Evaluation metrics are also proposed to assess the severity of the blind zone including laser beam density, blind zone height and duration, vehicle trajectory missing rate and duration. The simulation model can be used to evaluate and identify optimal configurations for different installation scenarios. To validate the proposed model, the 15-min US101 NGSIM (Next Generation SIMulation) dataset was used for validating the proposed model. Different configuration settings were simulated and compared. The evaluation results demonstrate the capabilities of the proposed models in planning for optimal roadside LiDAR sensor installation for vehicle detection and tracking.

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Accurate detection of vehicle, pedestrian, cyclist and wheelchair from roadside light detection and ranging sensors

Zhao

Chen

et al. 2023

Journal of Intelligent Transportation Systems

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Traffic Volume Detection Using Infrastructure-Based LiDAR under Different Levels of Service Conditions

Cited by 4 publications

References 27 publications

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction

Roadside LiDAR Sensor Configuration Assessment and Optimization Methods for Vehicle Detection and Tracking in Connected and Automated Vehicle Applications

Accurate detection of vehicle, pedestrian, cyclist and wheelchair from roadside light detection and ranging sensors

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