Abstract: A method is provided. The method includes generating a set of candidate training data based on a simulated environment and a first set of environmental parameters. The method also includes training a machine learning model based on the set of candidate training data. The method further includes obtaining a set of segmentations based on the machine learning model and a set of test data. The method further includes determining whether a mean intersection-over-union (MIOU) of the set of segmentations has increased by more than a threshold change. The method further includes generating a next set of candidate training data based on the simulated environment and a second set of environmental parameters for the simulated environment, in response to determining that the MIOU has increased by more than the threshold MIOU amount.

Abstract: In one embodiment, a method is provided. The method includes generating a set of candidate training data based on a simulated environment and a first set of environmental parameters. The method also includes training a machine learning model based on the set of candidate training data. The method further includes obtaining a set of segmentations based on the machine learning model and a set of test data. The method further includes determining whether a mean intersection-over-union (MIOU) of the set of segmentations has increased by more than a threshold change. The method further includes generating a next set of candidate training data based on the simulated environment and a second set of environmental parameters for the simulated environment, in response to determining that the MIOU has increased by more than the threshold MIOU amount.

Abstract: Technologies and techniques for operating a sensor system. First sensor data is received that is generated using a first sensor. Second sensor data is received that is generated using a second sensor, wherein the first sensor data includes a first operational characteristic capability, and the second sensor data includes a second operational characteristic capability. A machine-learning model may be trained/applied, wherein the machine-learning model is trained to output the second sensor data based on input of the first sensor data. New sensor data is generated using the applied machine-learning model. A loss function may be applied to the new sensor data to determine the accuracy of the new sensor data relative to the first sensor data and the second sensor data.

Abstract: A system and methodologies for controlling the content of a heads up display to provide increased visibility of naturally occurring contours and surfaces in the environment is provided. Depth information is provided along with predefined lighting conditions in order to output an image by the HUD with enhanced feature contours.

Abstract: Autonomous driving systems may be provided with one or more sensors configured to capture perception data, a model configured to be continually trained in the transportation vehicle and a scalable subset memory configured to store a subset of a dataset previously used to train a model. A processor may be provided for continually training the model in the transportation vehicle using captured perception data previously unseen and the subset and for generating a new subset of data to be stored so that the model avoids catastrophic forgetting.

Abstract: A system and methodologies for controlling the content of a heads up display to provide increased visibility of naturally occurring contours and surfaces in the environment is provided. Depth information is provided along with predefined lighting conditions in order to output an image by the HUD with enhanced feature contours.

Abstract: Objects are modeled and rendered using multiple surfaces to provide attributes used in rendering. In some embodiments, a reference surface for an object is defined, e.g., using conventional modeling techniques. One or more auxiliary surfaces are associated with portions of the reference surface. Some of the surface attributes (e.g., color, surface normal, texture, lighting) are associated with the reference surface, while other attributes (e.g., transparency) are associated with the cards. To render an image, a ray associated with a pixel is traced to its intersection with the reference surface and to its intersection with one of the auxiliary surfaces. The attributes associated with the reference surface are determined based on the intersection point of the ray with the reference surface, and the attributes associated with the auxiliary surface are determined based on the intersection point of the ray with the auxiliary surface.