Vehicle Tracking and Speed Estimation From Roadside Lidar

Zhang, Jiaxing; Xiao, Wen; Coifman, Benjamin; Mills, J. P.

doi:10.1109/jstars.2020.3024921

Cited by 114 publications

(46 citation statements)

References 26 publications

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“…In this analysis, the conflict decision was based upon comparing the predefined paths and the vehicle dynamics on the path is described as a one-dimension model as follows:

where x and v are the longitudinal position and velocity of the vehicle and a is the acceleration input to the vehicle. Considering the fact that there are works about speed estimation based on only LiDAR sensors, we assume that LiDAR sensors can obtain vehicle velocity [ 28 ].…”

Section: Required Data Rate On V2i For Safe Crossingmentioning

confidence: 99%

See 1 more Smart Citation

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Fukatsu

Sakaguchi

2021

Sensors

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The development of automated driving is actively progressing, and connected cars are also under development. Connected cars are the technology of connecting vehicles to networks so that connected vehicles can enhance their services. Safety services are among the main services expected in connected car society. Cooperative perception belongs to safety services and improves safety by visualizing blind spots. This visualization is achieved by sharing sensor data via wireless communications. Therefore, the number of visualized blind spots highly depends upon the performance of wireless communications. In this paper, we analyzed the required sensor data rate to be shared for the cooperative perception in order to realize safe and reliable automated driving in an intersection scenario. The required sensor data rate was calculated by the combination of recognition and crossing decisions of an automated driving vehicle to adopt realistic assumptions. In this calculation, CVFH was used to derive tight requirements, and the minimum required braking aims to alleviate the traffic congestion around the intersection. At the end of the paper, we compare the required sensor data rate with the outage data rate realized by conventional and millimeter-wave communications, and show that millimeter-wave communications can support safe crossing at a realistic velocity.

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“…In this analysis, the conflict decision was based upon comparing the predefined paths and the vehicle dynamics on the path is described as a one-dimension model as follows:

Section: Required Data Rate On V2i For Safe Crossingmentioning

confidence: 99%

“…where x and v are the longitudinal position and velocity of the vehicle and a is the acceleration input to the vehicle. Considering the fact that there are works about speed estimation based on only LiDAR sensors, we assume that LiDAR sensors can obtain vehicle velocity [28].…”

Section: Cooperative Perception and Intersection Scenario Descriptionmentioning

confidence: 99%

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Fukatsu

Sakaguchi

2021

Sensors

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show abstract

“…J. Z. et al achieved tracking and speed estimation of vehicles at intersections using 32-line lidar with a speed estimation accuracy of 0.22 m/s [ 28 ]. Z.…”

Section: Introductionmentioning

confidence: 99%

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Bai

Zhang

et al. 2021

Sensors

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Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

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“…With the development of light detection and ranging (LiDAR) technology, mobile laser scanning (MLS) systems, which deploy one or multiple LiDARs on a ground-based vehicle, 1 can quickly collect a high-resolution and high-precision point cloud of the surroundings of the vehicle and have gained increasing attention in three-dimensional (3D) urban scene analysis, 2 including urban 3D modeling 3 and automated urban driving. 4 Classification of MLS point clouds, in which each point in an MLS point cloud is determined to belong to a specific class, e.g., ground, 5 road, 6 road markings, 7 vehicles, 8 power lines, 9 and street trees, 10,11 is a common and core task for various applications of 3D urban scene analysis. 12 Weinmann et al 13 proposed a pointwise classification framework, whereby each individual point is classified by a binary classifier involving a set of local geometric features derived from the neighborhoods of the point.…”

Section: Introductionmentioning

confidence: 99%

Pointwise classification of mobile laser scanning point clouds of urban scenes using raw data

Yuan

Lin

et al. 2021

J. Appl. Rem. Sens.

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Mobile laser scanning (MLS), which can quickly collect a high-resolution and highprecision point cloud of the surroundings of a vehicle, is an appealing technology for threedimensional (3D) urban scene analysis. In this regard, the classification of MLS point clouds is a common and core task. We focus on pointwise classification, in which each individual point is categorized into a specific class by applying a binary classifier involving a set of local features derived from the neighborhoods of the point. To speed up the neighbor search and enhance feature distinctiveness for pointwise classification, we exploit the topological and semantic information in the raw data acquired by light detection and ranging (LiDAR) and recorded in scan order. First, a two-dimensional (2D) scan grid for data indexing is recovered, and the relative 3D coordinates with respect to the LiDAR position are calculated. Subsequently, a set of local features is extracted using an efficient neighbor search method with a low computational complexity independent of the number of points in a point cloud. These features are further merged to produce a variety of binary classifiers for specific classes via a GentleBoost supervised learning algorithm combining decision trees. The experimental results on the Paris-rue-Cassette database demonstrate that the proposed approach outperforms the state-of-the-art methods with a 10% improvement in the F 1 score, whereas it uses simpler geometric features derived from a spherical neighborhood with a radius of 0.5 m. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Vehicle Tracking and Speed Estimation From Roadside Lidar

Cited by 114 publications

References 26 publications

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

Pointwise classification of mobile laser scanning point clouds of urban scenes using raw data

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