University of California

Luminar Technologies, Inc.

Luminar, LLC

Akshay Rangesh

Benjamin Englard

Pranav Maheshwari

Vahid R Ramezani

Meseret R Gebre

Siddhesh S Mhatre

Mohan M Trivedi

Nachiket Deo

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 surround multi-object tracking and surround vehicle motion prediction framework is provided. A full-surround camera array and LiDAR sensor based approach provides for multi-object tracking for autonomous vehicles. The multi-object tracking incorporates a fusion scheme to handle object proposals from the different sensors within the calibrated camera array. A motion prediction framework leverages the instantaneous motion of vehicles, an understanding of motion patterns of freeway traffic, and the effect of inter-vehicle interactions. The motion prediction framework incorporates probabilistic modeling of surround vehicle trajectories. Additionally, subcategorizing trajectories based on maneuver classes leads to better modeling of motion patterns. A model takes into account interactions between surround vehicles for simultaneously predicting each of their motion.

Surround vehicle tracking and motion prediction

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.

Growing community of inventors

San Diego, CA, United States of America

Akshay Rangesh