Uatc, LLC

Gellert Sandor Mattyus

Raquel Urtasun

Shenlong Wang

Min Bai

Namdar Homayounfar

Shrindihi Kowshika Lakshmikanth

Wei-Chiu Ma

Julieta Martinez Covarrubias

Ioan Andrei Barsan

Hongbo Fan

Alexandre Doubov

Shrinidihi Kowshika Lakshmikanth

Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain a first type of sensor data (e.g., camera image data) associated with a surrounding environment of an autonomous vehicle and/or a second type of sensor data (e.g., LIDAR data) associated with the surrounding environment of the autonomous vehicle. The computing system can generate overhead image data indicative of at least a portion of the surrounding environment of the autonomous vehicle based at least in part on the first and/or second types of sensor data. The computing system can determine one or more lane boundaries within the surrounding environment of the autonomous vehicle based at least in part on the overhead image data indicative of at least the portion of the surrounding environment of the autonomous vehicle and a machine-learned lane boundary detection model.

Autonomous vehicle lane boundary detection systems and methods

A computer-implemented method for localizing a vehicle can include accessing, by a computing system comprising one or more computing devices, a machine-learned retrieval model that has been trained using a ground truth dataset comprising a plurality of pre-localized sensor observations. Each of the plurality of pre-localized sensor observations has a predetermined pose value associated with a previously obtained sensor reading representation. The method also includes obtaining, by the computing system, a current sensor reading representation obtained by one or more sensors located at the vehicle. The method also includes inputting, by the computing system, the current sensor reading representation into the machine-learned retrieval model. The method also includes receiving, by the computing system and from the machine-learned retrieval model, a determined current pose value for the vehicle based at least in part on one or more of the pre-localized sensor observations determined to be a closest match to the current sensor reading representation. The determined current pose value has an accuracy of within about one meter.

Localization with diverse dataset for autonomous vehicles

Systems and methods for facilitating communication with autonomous vehicles are provided. In one example embodiment, a computing system can obtain rasterized LIDAR data associated with a surrounding environment of an autonomous vehicle. The rasterized LIDAR data can include LIDAR image data that is rasterized from a LIDAR point cloud. The computing system can access data indicative of a machine-learned lane boundary detection model. The computing system can input the rasterized LIDAR data associated with the surrounding environment of the autonomous vehicle into the machine-learned lane boundary detection model. The computing system can obtain an output from the machine-learned lane boundary detection model. The output can be indicative of one or more lane boundaries within the surrounding environment of the autonomous vehicle.

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Gellert Sandor Mattyus