Plusai Limited

Yibo Chen

Mianwei Zhou

Abhishek Bhatia

Kuan-Wei Liu

In an embodiment, a method comprises detecting, at a processor of an autonomous vehicle, a discrepancy between a map and a property sensed by at least one sensor onboard the autonomous vehicle, the property being associated with an external environment of the autonomous vehicle. In response to detecting the discrepancy, and based on the discrepancy, an annotation for the map is generated via the processor. A signal representing the annotation is caused to be transmit to a compute device that is remote from the autonomous vehicle. A signal representing a map update is received from the compute device that is remote form the autonomous vehicle. The map update is generated based on the annotation, the map update (1) including replacement information for a region of the map associated with the annotation, and (2) not including replacement information for a remainder of the map.

Methods and apparatus for navigating an autonomous vehicle based on a map updated in regions

Methods, systems, and non-transitory computer-readable media are configured to perform operations comprising determining lane detection data and object detection data associated with an environment; determining a lane template based on the lane detection data; and generating localization data that identifies a location of an object in the environment based on the lane template and the object detection data.

Vehicle localization based on lane templates

Embodiments provide improved techniques for identifying object and vehicle locations on an HD map using camera images. Synthetic data may be generated by populating a high-definition (HD) map with an object. The populated HD map can be projected to a two-dimensional camera view image depicting the object. The HD map and object detection data generated from image (e.g., identifying lane/object locations within the image) can be used to train the model to identify HD map locations of an autonomous vehicle capturing images as it travels, as well as various objects detected from those captured images. Subsequently, a new image may be processed using object detection techniques to detect lane/object locations within the image. An HD map and the detected lane/object data may be provided to the model, which in turn identifies, on the HD map, the locations of the vehicle and the various objects.

Model-based localization on high-definition maps

Systems, methods, and non-transitory computer readable media can perform operations comprising generating a first type of pose and a second type of pose based on visual geometry localization; determining a mode for planning a planning path for a vehicle based on at least one of the first type of pose and the second type of pose; and generating the planning path for the vehicle based on the mode.

Multi-mode visual geometry localization

Systems, methods, and non-transitory computer-readable media can perform operations comprising determining visual geometry detections (e.g., lane line detections) associated with geometry corresponding with a map; aligning the visual geometry detections with the geometry based on transformations associated with selected degrees of freedom; and determining a pose of a vehicle based on alignment of the visual geometry detections with the geometry.

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San Jose, CA, United States of America

Yibo Chen