Machine learned interaction prediction from top-down representation

• Main Authors: Garimella, Gowtham, Huang, Aaron, Packer, Jefferson Bradfield
• Format: Patent
• Language: English
• Published: 13.08.2024
• Subjects: 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

Techniques are discussed for interaction probabilities associated with regions of an environment around a vehicle. An interaction probability of a region may indicate a likelihood an object positioned at the region will interact with the vehicle. A top-down multi-channel image representing a top-down view of the environment and objects therein may be generated and input to a machine learned (ML) model. The ML model may output a probability map, a portion of the probability map comprising a region and an interaction probability associated with the region that indicates a likelihood objects positioned at the region will interact with the vehicle. A priority for resource assignment or analysis may be determined based on the interaction probability for an object positioned in the region. Control of the vehicle may be performed based at least in part on the priority for resource assignment or analysis.