Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm

Jian, Wu; Tian, Yuan; Xu, Hao; Yue, Rui; Wang, Aobo; Song, Xiuguang

doi:10.1016/j.optlastec.2019.02.039

Cited by 39 publications

(19 citation statements)

References 21 publications

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“…The drivers may think no pedestrian will show up since the flashing light is off. As a result, the drivers may not slow down their speeds and this can cause the high vehicle-pedestrian crash risk [22]. Therefore, how to address this impact can be a topic for future studies.…”

Section: Discussionmentioning

confidence: 99%

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An Automatic Trigged Rectangular Rapid Flashing Beacons (RRFB) System Using the Roadside LiDAR Sensor

Cui

Jian

et al. 2019

IEEE Access

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The existing rectangular rapid flashing beacons (RRFB) could not be automatically trigged with acceptable accuracy. This paper introduces a strategy to develop an automatic trigged RRFB system using the roadside Light Detection and Ranging (LiDAR). The trajectories of the road users can be extracted in real-time with the proposed LiDAR data processing algorithms. The pedestrian/bicycle crossing intention was predicted with Naïve Bayes (NB) classifier. Different detection zones were provided to detect the pedestrian/bicycle crossing movements in real-time. The updated RRFB system was implemented at one intersection in Henderson, Nevada, United States. The results of the field test showed that the overall false rate of the updated RRFB system is very low (0.68%). The performance of the proposed method is superior compared to the state-of-the-art method. The updated RRFB system can greatly improve pedestrian safety at unsignalized intersections. INDEX TERMS Roadside LiDAR, vehicle-pedestrian conflicts, object tracking, rectangular rapid flashing beacons, pedestrian safety.

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Section: Discussionmentioning

confidence: 99%

“…But the horizontal detection angle for each radar is limited. To cover the 360-degree horizontal area, several radar sensors are required to work together [14]. How to merge the data obtained from several radar sensors into one is also another challenge.…”

Section: High-resolution Micro Traffic Data (Hrmtd) Collection Wimentioning

confidence: 99%

An Automatic Trigged Rectangular Rapid Flashing Beacons (RRFB) System Using the Roadside LiDAR Sensor

Cui

Jian

et al. 2019

IEEE Access

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“…This paper used two types of LiDAR: VLP-16 and VLP-32c for data collection. For the detailed parameters of VLP-16 and VLP-32c, we refer the readers to [23].…”

Section: Materials and Preprocessingmentioning

confidence: 99%

“…This paper used two types of LiDAR: VLP-16 and VLP-32c for data collection. For the detailed parameters of VLP-16 and VLP-32c, we refer the readers to [23]. But in theory, our proposed queue length detection method can work for any brand of rotating LiDAR after necessary calibration based on the different setting parameters of the LiDAR.…”

Section: Background Filteringmentioning

confidence: 99%

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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“…The new generation of transportation systems employ advanced communication technologies, such as 5G network [1]; dedicated short-range communication (DSRC) [2]; and sensing technologies, such as Light Detection and Ranging (LiDAR) and video sensors, to exchange each other's real-time status and to understand the surrounding traffic environment. When autonomous vehicle platforms can achieve real-time detection and control, connected-vehicle technologies allow drivers/vehicles to "see" across buildings, over surrounding vehicles and other obstacles [3,4]. The advantages of connected vehicles rely on vehicle2x wireless communication, including but not limited to vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-other-road users.…”

Section: Introductionmentioning

confidence: 99%

Data Registration with Ground Points for Roadside LiDAR Sensors

Yue

Jian

et al. 2019

Remote Sensing

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The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users’ trajectories for connected/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors’ data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors’ data on an autonomous vehicle or a robot.

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Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm

Cited by 39 publications

References 21 publications

An Automatic Trigged Rectangular Rapid Flashing Beacons (RRFB) System Using the Roadside LiDAR Sensor

An Automatic Trigged Rectangular Rapid Flashing Beacons (RRFB) System Using the Roadside LiDAR Sensor

Real-Time Queue Length Detection with Roadside LiDAR Data

Data Registration with Ground Points for Roadside LiDAR Sensors

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