Feature-based prediction

Feature-based prediction is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate event(s), such as a lane change, associated with agent(s) in the environment. The computing sys...

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Bibliographische Detailangaben
Hauptverfasser:	Yang, Daylen Guang Yu, Sapp, Benjamin John
Format:	Patent
Sprache:	eng
Schlagworte:	ANALOGOUS ARRANGEMENTS USING OTHER WAVES 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 CONTROLLING COUNTING DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES HANDLING RECORD CARRIERS LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES MEASURING PERFORMING OPERATIONS PHYSICS PRESENTATION OF DATA RADIO DIRECTION-FINDING RADIO NAVIGATION RECOGNITION OF DATA RECORD CARRIERS REGULATING ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT SIGNALLING SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES TESTING TRAFFIC CONTROL SYSTEMS TRANSPORTING VEHICLES IN GENERAL
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Beschreibung
Zusammenfassung:	Feature-based prediction is described. In an example, a vehicle can capture sensor data while traversing an environment and can provide the sensor data to computing system(s). The sensor data can indicate event(s), such as a lane change, associated with agent(s) in the environment. The computing system(s) can determine, based on the sensor data, a time associated with the event and can determine features associated with a period of time relative to the time of the event. In an example, the computing system(s) can aggregate the features with additional features associated with other similar events to generate training data and can train, based at least in part on the training data, a machine learned model for predicting new events. In an example, the machine learned model can be transmitted to vehicle(s), which can be configured to alter drive operation(s) based, at least partly, on output(s) of the machine learned model.