INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS

In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for...

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Bibliographische Detailangaben
Hauptverfasser:	Cvijetic, Neda, Sajjadi Mohammadabadi, Sayed Mehdi, Park, Minwoo, Hervas, Berta Rodriguez, Pham, Trung, Kwon, Junghyun, Dou, Hang, Nister, David, Tryndin, Igor
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 PERFORMING OPERATIONS PHYSICS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT SIGNALLING TRAFFIC CONTROL SYSTEMS TRANSPORTING VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:	In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.