Automatic background filtering and lane identification with roadside LiDAR data

Jian, Wu; Xu, Hao; Zheng, Jianying

doi:10.1109/itsc.2017.8317723

Cited by 66 publications

(47 citation statements)

References 9 publications

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“…The roadside LiDAR can be installed permanently (on the top of a pedestrian signal) or temporarily (on a tripod) for data collection [24]. The recommended height for LiDAR installation is 2-3 meters above the ground to avoid possible man-made destruction and to reduce occlusion issues considering the limited vertical field of view [25]. The scanning rate of the LiDAR is set as 10 Hz.…”

Section: Background Filteringmentioning

confidence: 99%

“…Background filtering is used to exclude the other irrelevant information (buildings, trees and ground points) and to keep the moving objects (vehicles, pedestrians and other road users) in the space at the same time. This paper applied a point density-based unsupervised algorithm named 3D-DSF developed by Wu et al [25] for background filtering. The 3D-DSF first integrated the data collected in a time period (such as 5 minutes of data) into one space based on the XYZ coordinates of the LiDAR points.…”

Section: Background Filteringmentioning

confidence: 99%

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Real-Time Queue Length Detection with Roadside LiDAR Data

Zhang

et al. 2020

Sensors

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Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.

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Section: Background Filteringmentioning

confidence: 99%

Section: Background Filteringmentioning

confidence: 99%

Real-Time Queue Length Detection with Roadside LiDAR Data

Zhang

et al. 2020

Sensors

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“…More specifically speaking, the background includes buildings, trees, as well as ground points. Background filtering is to identify and exclude backgrounds, which is an important step for LiDAR point reduction [35]. The background filtering can accelerate the following data processing steps by removing the irrelevant points from the space.…”

Section: A Trajectory Extraction Proceduresmentioning

confidence: 99%

“…Any points detected in the 3D array are identified as background points and are excluded from the space. The detailed background algorithm can be found from an earlier version [35] and a later improved version [36].…”

Section: A Trajectory Extraction Proceduresmentioning

confidence: 99%

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Sun

et al. 2019

IEEE Access

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Vehicle-pedestrian conflicts have been the major concern for traffic safety. Surrogate safety measures are widely applied for pedestrian safety evaluation. However, how to quickly identify the vehiclepedestrian surrogate safety measures at the individual site is challenging due to the difficulty of obtaining the high-resolution trajectories of road users. This paper presented an effective method to generate the highresolution traffic trajectories from the roadside deployed Light Detection and Ranging (LiDAR) sensor. The vehicle-pedestrian conflicts can then be identified from the trajectories simply using the speed-distance profile (SDP) of the vehicles. The SDP can be used to develop a rule-based method for vehicle-pedestrian identification. The events can be divided into different risk levels based on the spatial distribution of the SDP. The case study shows that the rule-based method can detect vehicle-pedestrian near-crash events effectively. The other indicators, such as widely used time-to-collision (TTC) or deceleration rate to avoid a crash (DRAC), can be also obtained from the SDP. The engineers can also adjust the thresholds in the rule-based method to meet the specific requirements at different sites. The proposed method can be extended to identify vehicle-vehicle conflicts or vehicle-bicycle conflicts in future studies. INDEX TERMS Roadside LiDAR, vehicle-pedestrian conflicts, surrogate safety measures, high-resolution trajectories, pedestrian safety.

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“…trees, buildings, ground surface, etc.) which are mixed with road users' points in each frame [20], e.g., the density based method [21], [22]. By background filtering, only road users' points are left.…”

Section: Lidar Data and Pre-processingmentioning

confidence: 99%

Tracking Multi-Vehicles With Reference Points Switches at the Intersection Using a Roadside LiDAR Sensor

Zhang

Sun

et al. 2019

IEEE Access

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Obtaining real-time trajectories with high accuracy and high resolution of connected and unconnected vehicles with roadside LiDAR (Light Detecting and Ranging) sensors can highly benefit connected vehicle (CV) systems. In order to track vehicles with a roadside LiDAR sensor, the vehicle-corner point with the shortest distance to the sensor is usually used as the reference point since only parts of the vehicle can be scanned, but trajectory interruptions and speed errors caused by neglecting reference point switch were observed. With the movement of a vehicle, the reference point may switch from one corner point to another, especially at the intersection where it may switch more than once when turning left/right. Moreover, the vehicles with various lengths will increase the impacts of reference point switch on tracking. This paper proposes a tracking method considering reference point switch and occlusions for multi-vehicles at the intersection based on a roadside LiDAR sensor. In particular, reference point switch patterns are deeply explored and trajectory prediction for occlusions based on Kalman filtering method is developed. Then they are integrated into a global nearest neighbor (GNN) method for pairing reference points between frames. At last, the proposed method was validated by LiDAR data collected by a 32-channel 360-degree LiDAR sensor at the intersection of McCarran Blvd and Evans Ave in the city of Reno, Nevada, U.S. The new approach correctly captured switches of vehicle reference points and solved short-term occlusion issues to avoid trajectory interruptions, speed errors, and corner point misrecognitions. INDEX TERMS Roadside LiDAR sensor, vehicle tracking, reference point switch, vehicle occlusion.

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Automatic background filtering and lane identification with roadside LiDAR data

Cited by 66 publications

References 9 publications

Real-Time Queue Length Detection with Roadside LiDAR Data

Real-Time Queue Length Detection with Roadside LiDAR Data

Automatic Vehicle-Pedestrian Conflict Identification With Trajectories of Road Users Extracted From Roadside LiDAR Sensors Using a Rule-Based Method

Tracking Multi-Vehicles With Reference Points Switches at the Intersection Using a Roadside LiDAR Sensor

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