DISTANCE ESTIMATION TO OBJECTS AND FREE-SPACE BOUNDARIES IN AUTONOMOUS MACHINE APPLICATIONS

In various examples, a deep neural network (DNN) is trained-using image data alone-to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicl...

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Bibliographische Detailangaben
Hauptverfasser:	Oh, Sangmin, Park, Minwoo, Kwon, Junghyun, Yang, Yilin, Nister, David, Jujjavarapu, Bala Siva Sashank, Ye, Zhaoting
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES COUNTING ELECTRIC DIGITAL DATA PROCESSING PERFORMING OPERATIONS PHYSICS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT TRANSPORTING VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:	In various examples, a deep neural network (DNN) is trained-using image data alone-to accurately predict distances to objects, obstacles, and/or a detected free-space boundary. The DNN may be trained with ground truth data that is generated using sensor data representative of motion of an ego-vehicle and/or sensor data from any number of depth predicting sensors-such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. The DNN may be trained using two or more loss functions each corresponding to a particular portion of the environment that depth is predicted for, such that-in deployment-more accurate depth estimates for objects, obstacles, and/or the detected free-space boundary are computed by the DNN.