Abstract
In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical on-road scenes, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle’s type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability, where CAVS stands for Center for Advanced Vehicular Systems) dataset and is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz</uri> .
Highlights
Either from the point of research or from the point of its applicability in fields such as robotics, forest studies, military, etc., autonomous driving in off-road environment is gaining increasing attention nowadays
To demonstrate the above concept and help the research community, we propose a new dataset for semantic road segmentation with fine partitioning for off-road autonomous driving based on the traversing capability of three different vehicles
It is highly probable that, along with the actual track bearing the characters of off-road, some part of the vegetation and forest may come as a traversable path that were to be classified into separate classes under semantic segmentation concept
Summary
Either from the point of research or from the point of its applicability in fields such as robotics, forest studies, military, etc., autonomous driving in off-road environment is gaining increasing attention nowadays. The available datasets have been labeled using standard pixel segmentation that identify classes of objects in a scene but do not consider the relationship between types of challenges on off-road trails and the capabilities of different vehicles. To address these limitations, we propose a segmentation approach, and corresponding dataset, that is based on the concept of traversability that is mediated by class of vehicle. To demonstrate the above concept and help the research community, we propose a new dataset for semantic road segmentation with fine partitioning for off-road autonomous driving based on the traversing capability of three different vehicles. Images with typical features are selected from the rosbag extractions and provided to the labelers to annotate with the traversability labels
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
