Luminar Technologies, Inc.

Luminar, LLC

Meseret R Gebre

Pranav Maheshwari

Vahid R Ramezani

Benjamin Englard

Shubham C Khilari

Akshay Rangesh

Siddhesh S Mhatre

Austin K Russell

István Peter Burbank

A method for determining a scan pattern according to which a sensor equipped with a scanner scans a field of regard (FOR) is presented. The method comprises obtaining, by processing hardware, a plurality of objective functions, each of the objective functions specifying a cost for a respective property of the scan pattern, expressed in terms of one or more operational parameters of the scanner. The method further includes applying, by the processing hardware, an optimization scheme to the plurality of objective functions to generate the scan pattern. The method further includes scanning the FOR according to the generated scan pattern.

Generating scan patterns using cognitive lidar

A scanning imaging sensor is configured to sense an environment through which a vehicle is moving. A method for determining one or velocities associated with objects in the environment includes generating features from the first set of scan lines and the second set of scan lines, the two sets corresponding to two instances in time. The method further includes generating a collection of candidate velocities based on feature locations and time differences, the features selected pairwise with one from the first set and another from the second set. Furthermore, the method includes analyzing the distribution of candidate velocities, for example, by identifying one or more modes from the collection of the candidate velocities.

Velocity determination with a scanned lidar system

A sequence of images generated at respective times by one or more sensors configured to sense an environment through which objects are moving relative to the one or more sensors is received. A message passing graph having a multiplicity of layers associated with the sequence of images is constructed. A neural network supported by the message passing graph is trained. The training includes performing a pass through the message passing graph in a forward direction including by adding a new feature node based on a feature detection and a new edge node and performing a pass through the message passing graph in a backward direction, including by updating at least one edge node of the message passing graph. Multiple features are tracked through the sequence of images, including passing messages through the message passing graph.

Neural network for object detection and tracking

A method for multi-object tracking includes receiving a sequence of images generated at respective times by one or more sensors configured to sense an environment through which objects are moving relative to the one or more sensors, and constructing a message passing graph in which each of a multiplicity of layers corresponds to a respective one in the sequence of images. The method also includes tracking multiple features through the sequence of images, including passing messages in a forward direction and a backward direction through the message passing graph to share information across time.

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Palo Alto, CA, United States of America

Meseret R Gebre