Uatc, LLC

Aurora Operations, Inc.

Min Bai

Raquel Urtasun

Shenlong Wang

Namdar Homayounfar

Gellert Sandor Mattyus

Shrindihi Kowshika Lakshmikanth

Ming Liang

Bin Yang

Wei-Chiu Ma

Wenyuan Zeng

Shrinidihi Kowshika Lakshmikanth

Systems and methods for identifying travel way features in real time are provided. A method can include receiving two-dimensional and three-dimensional data associated with the surrounding environment of a vehicle. The method can include providing the two-dimensional data as one or more input into a machine-learned segmentation model to output a two-dimensional segmentation. The method can include fusing the two-dimensional segmentation with the three-dimensional data to generate a three-dimensional segmentation. The method can include storing the three-dimensional segmentation in a classification database with data indicative of one or more previously generated three-dimensional segmentations. The method can include providing one or more datapoint sets from the classification database as one or more inputs into a machine-learned enhancing model to obtain an enhanced three-dimensional segmentation. And, the method can include identifying one or more travel way features based at least in part on the enhanced three-dimensional segmentation.

System and method for identifying travel way features for autonomous vehicle motion control

Techniques for improving the performance of an autonomous vehicle (AV) by automatically annotating objects surrounding the AV are described herein. A system can obtain sensor data from a sensor coupled to the AV and generate an initial object trajectory for an object using the sensor data. Additionally, the system can determine a fixed value for the object size of the object based on the initial object trajectory. Moreover, the system can generate an updated initial object trajectory, wherein the object size corresponds to the fixed value. Furthermore, the system can determine, based on the sensor data and the updated initial object trajectory, a refined object trajectory. Subsequently, the system can generate a multi-dimensional label for the object based on the refined object trajectory. A motion plan for controlling the AV can be generated based on the multi-dimensional label.

Automatic annotation of object trajectories in multiple dimensions

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

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.

Systems and methods for detecting objects are provided. In one example, a computer-implemented method includes receiving sensor data from one or more sensors configured to generate sensor data. The method includes inputting the sensor data to a machine-learned model that generates a class prediction and an instance prediction for each of a plurality of portions of the sensor data. The instance prediction includes an energy value based on a distance to at least one object boundary. The machine learned model can be trained to generate a common energy value to represent the at least one object boundary. The method includes generating as outputs of the machine-learned model, an instance prediction and a class prediction corresponding to each of the plurality of portions of the sensor data. The method includes generating one or more object segments based at least in part on the instance predictions and the class predictions.

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Toronto, Canada

Min Bai