Vehicle Detection under Adverse Weather from Roadside LiDAR Data

Jian, Wu; Xu, Hao; Tian, Yuan; Pi, Rendong; Yue, Rui

doi:10.3390/s20123433

Cited by 42 publications

(25 citation statements)

References 41 publications

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“…remains unclear. Wu et al [51] use the point density filtering method with enhanced DBSCAN algorithm to improve the performance of the LiDAR in windy and snowy weather, but for the case of foggy weather further research on the applicability of this approach is also required.…”

Section: The Influence Of Harsh Weather and The Environment On The Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

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The paper proposes an approach to assessing the allowed signal-to-noise ratio (SNR) for light detection and ranging (LiDAR) of unmanned autonomous vehicles based on the predetermined probability of false alarms under various intentional and unintentional influencing factors. The focus of this study is on the relevant issue of the safe use of LiDAR data and measurement systems within the “smart city” infrastructure. The research team analyzed and systematized various external impacts on the LiDAR systems, as well as the state-of-the-art approaches to improving their security and resilience. It has been established that the current works on the analysis of external influences on the LiDARs and methods for their mitigation focus mainly on physical (hardware) approaches (proposing most often other types of modulation and optical signal frequencies), and less often software approaches, through the use of additional anomaly detection techniques and data integrity verification systems, as well as improving the efficiency of data filtering in the cloud point. In addition, the sources analyzed in this paper do not offer methodological support for the design of the LiDAR in the very early stages of their creation, taking into account a priori assessment of the allowed SNR threshold and probability of detecting a reflected pulse and the requirements to minimize the probability of “missing” an object when scanning with no a priori assessments of the detection probability characteristics of the LiDAR. The authors propose a synthetic approach as a mathematical tool for designing a resilient LiDAR system. The approach is based on the physics of infrared radiation, the Bayesian theory, and the Neyman–Pearson criterion. It features the use of a predetermined threshold for false alarms, the probability of interference in the analytics, and the characteristics of the LiDAR’s receivers. The result is the analytical solution to the problem of calculating the allowed SNR while stabilizing the level of “false alarms” in terms of background noise caused by a given type of interference. The work presents modelling results for the “false alarm” probability values depending on the selected optimality criterion. The efficiency of the proposed approach has been proven by the simulation results of the received optical power of the LiDAR’s signal based on the calculated SNR threshold and noise values.

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Section: The Influence Of Harsh Weather and The Environment On The Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

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“…The Global Nearest Neighbor (GNN) was employed for object tracking. This tracking algorithm utilizes the geometric location information of the vehicle to identify key points (nearest point to the LiDAR) in different frames belonging to the same object [36]. For each object in the current frame, the algorithm searches for the object with the minimum distance to the object in the previous frame.…”

Section: Lidar Data Processingmentioning

confidence: 99%

Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network

Zhang

Ma³

et al. 2021

Electronics

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Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper.

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“…The “long short-term memory recurrent neural network” was employed here to help predict the flow of the traffic, with the help of the data from the air pollution and atmospheric condition. Similarly in [ 8 , 9 ], the performance of several data processing algorithms that is geared to roadside light detection and ranging under unknown weather conditions was evaluated, and a background filtering and object clustering method was developed for the purpose of processing the roadside light detection and ranging data under the unknown weather conditions. It was shown that the current processing algorithm for the roadside light detection and ranging was based on assuming known weather conditions.…”

Section: Summary Of the Special Issuementioning

confidence: 99%