Vehicle Detection under Adverse Weather from Roadside LiDAR Data.
Sensors (Basel)
; 20(12)2020 Jun 17.
Article
em En
| MEDLINE
| ID: mdl-32560568
ABSTRACT
Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.
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Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article