Abstract: Systems and methods are presented herein for generating a three-dimensional model based on data from one or more two-dimensional images to identify a traversable space for a vehicle and objects surrounding the vehicle. A bounding area is generated around an object identified in a two-dimensional image captured by one or more sensors of a vehicle. Semantic segmentation of the two-dimensional image is performed based on the bounding area to differentiate between the object and a traversable space. The three-dimensional model of an environment comprised of the object and the traversable space is generated based on the semantic segmentation. The three-dimensional model is used for one or more of processing or transmitting instructions useable by one or more driver assistance features of the vehicle.

Abstract: Systems and methods for determining the drivable space of a road, for applications such as autonomous navigation. To determine the non-drivable space under another vehicle, systems and methods of embodiments of the disclosure generate 3D bounding boxes from 2D bounding boxes of objects in captured roadway images, and from various geometric constraints. Image portions may be labeled as drivable or non-drivable according to projections of these 3D bounding boxes onto their road surfaces. These labeled images, along with accompanying semantic information, may be compiled to form training datasets for a machine learning model such as a CNN. The training datasets may train the CNN to classify input image portions into drivable and non-drivable space, for applications such as autonomous navigation.

Abstract: Systems and methods for determining the drivable space of a road, for applications such as autonomous navigation. To determine the non-drivable space under another vehicle, systems and methods of embodiments of the disclosure generate 3D bounding boxes from 2D bounding boxes of objects in captured roadway images, and from various geometric constraints. Image portions may be labeled as drivable or non-drivable according to projections of these 3D bounding boxes onto their road surfaces. These labeled images, along with accompanying semantic information, may be compiled to form training datasets for a machine learning model such as a CNN. The training datasets may train the CNN to classify input image portions into drivable and non-drivable space, for applications such as autonomous navigation.