Rivian IP Holdings, LLC

Aziz Umit Batur

Gary Fay

Sowmya Gade

Oliver Jeromin

Patrick Hunt

Kiran Ram Bharwani

Systems and methods for asynchronous late-fusion of measurements. State information and intermediate values may be calculated as measurements arrive and are stored. When late sensor measurements arrive out of the temporal order in which the measurements were generated, the late measurements are stored in temporal order rather than the order in which measurements arrive. State information is then recalculated to account for the late-arriving sensor measurement, with state outputs propagated forward in temporal order using the previously computed intermediate values to speed up computation. In this manner, more accurate revised state information is efficiently generated, accounting for any late-arriving measurements. This modular processing framework also enables sensors to be added or changed, which may cause measurements to arrive asynchronously, without having to reprogram the processing framework.

Modular architecture for asynchronous late-fusion of objects

A sensor fusion system and method are disclosed. One or more processors are operable to receive a plurality of object detection measurements from a plurality of sensors. Each of the plurality of object detection measurements are associated with a potential object detection track. A plurality of sensor confidence values associated with each of the plurality of sensors are received. A track confidence value is determined for each of the potential object detection tracks based on the received plurality of object detection measurements and the received plurality of sensor confidence values. An object detection for a potential object detection track that has a determined track confidence value meeting a predetermined detection threshold is then determined, or confirmed, and stored in a memory for subsequent use, and is relatively unaffected by a measurement from a sensor that has a field of view that omits or is occluded with respect to the given object detection track.

Systems and methods for bayesian likelihood estimation of fused objects

Various disclosed embodiments include methods of locating an intended passenger of an automotive vehicle and vehicle-based systems for locating an intended passenger of an automotive vehicle. In an illustrative embodiment, a method of locating an intended passenger of an automotive vehicle includes: moving an automotive vehicle to an approximate location of an intended passenger based on a location signal from the intended passenger received via a wireless network; after moving to the approximate location of the intended passenger, receiving, by the vehicle, an image from an electronic device of the intended passenger, the image being an image of the intended passenger or an image of local physical surroundings of the intended passenger; processing the image to determine additional location information of the intended passenger based on matching the image against one or more reference images; and accepting by the vehicle, the intended passenger responsive to the image processing.

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Torrance, CA, United States of America

Aziz Umit Batur