Drivable Area Detection: a Comparative Study of Algorithms Based on Deep Learning

Abstract

Early techniques, frequently based on computer vision, classified drivable zones into four types: sensor-based, rule-based, map-based, and traditional machine learning (ML). Due to the limitations of these methods, they were later replaced by deep learning-based methods. The advancements in methods for segmenting drivable areas, encompassing both conventional computer vision-based approaches and deep learning-based algorithms are discussed in this article. This paper aims to conduct comparative research on the principles underlying Fast-SCNN, EDANet, and D3NET algorithms along with their performances on the CityScapes dataset. The accuracy and efficiency of the drivable area segmentation task deserve significant attention. Furthermore, a comparative study is conducted separately on PC and embedded platforms to facilitate readers in selecting appropriate datasets and validation platforms. Lastly, the existing challenges as well as future trends pertaining to these algorithms are discussed. This study's comparisons offer valuable insights into the strengths and limitations of the reviewed methods, thereby aiding in the construction of the algorithm model.