Narit Hnoohom1, Sakorn Mekruksavanich2, Anuchit Jitpattanakul3,4,*
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1275-1291, 2023, DOI:10.32604/iasc.2023.038584
Abstract In recent years, as intelligent transportation systems (ITS) such as autonomous driving and advanced driver-assistance systems have become more popular, there has been a rise in the need for different sources of traffic situation data. The classification of the road surface type, also known as the RST, is among the most essential of these situational data and can be utilized across the entirety of the ITS domain. Recently, the benefits of deep learning (DL) approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual methods. The ability to extract important features is vital in making RST… More >
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