01529nas a2200157   4500008004100000245008800041210006900129520094300198653002301141653001601164653002001180653001701200100002001217700002101237856011301258       2020                        eng d00aHyperparameter Optimization using Grid Search for use in Monocular Depth Estimation0 aHyperparameter Optimization using Grid Search for use in Monocul3 a<p>Autonomous vehicles uses a myriad of sensors to observe and monitor its surroundings. Current re- search incorporates static digital images that are used to train and optimize deep neural networks. These deep neural networks are used to identify and determine the distances of objects from the autonomous vehicle. Unfortunately, the hyperparameters used to train these networks were chosen arbitrarily. We explored the hyperparameter space through a two-step grid search in order to recognize the optimal combination of losses before continuing to train the rest of the system to understand the trade offs be- tween constant parameters. Specifically, the research focuses on finding the optimal relation between the left-right consistency, appearance, disparity losses in generating accurate feature edges in our output images. The trade off between costs allows for the neural network an increased performance at edge detection.</p>
10aAutonomous Driving10aGrid Search10aNeural Networks10aOptimization1 aBarrett, Calvin1 aBessho, Tomohide  uhttps://csl.arizona.edu/content/hyperparameter-optimization-using-grid-search-use-monocular-depth-estimation