Uatc, LLC

Nyfix, Inc.

Nyse Group, Inc.

Aurora Operations, Inc.

Convergex Execution Solutions LLC

Peter Hansen

Robert Zlot

David Prasser

Lars Kragh

Nicolay Landmark

Ethan Eade

Rachel Ahn

Brett Browning

Xxx Xinjilefu

Hatem Alismail

Bryan John Nagy

David LaRose

Brett Bavar

Adam Cole Panzica

Adam Henry Polk Milstein

Gordon Peter Bailey

James Andrew Bagnell

Narek Melik-Barkhudarov

Adam Milstein

Jennifer Joyce Padgett

David Larose

Various examples are directed to systems and methods for locating a vehicle. A pose state estimator may access a previous position for the vehicle at a first time, wherein the previous position is on a first sub-map of a plurality of sub-maps. The pose state estimator may receive from a first localizer a first position estimate for the vehicle at a second time after the first time. The first position estimate may be on a second sub-map of the plurality of sub-maps. The pose state estimator may send to a second localizer a sub-map change message indicating the second sub-map.

Map selection for vehicle pose system

A system for determining a pose of a vehicle and building maps from vehicle priors processes received ground intensity LIDAR data including intensity data for points believed to be on the ground and height information to form ground intensity LIDAR (GIL) images including pixels in 2D coordinates where each pixel contains an intensity value, a height value, and x- and y-gradients of intensity and height. The GIL images are formed by filtering aggregated ground intensity LIDAR data falling into a same spatial bin on the ground and using a registration algorithm to align two GIL images relative to one another by estimating a 6-degree-of-freedom pose with associated uncertainty that minimizes error between the two GIL images. The aligned GIL images are provided as a pose estimate to a localizer. The system may provide online localization and pose estimation, prior building, and prior to prior alignment pose estimation using image-based techniques.

Ground intensity LIDAR localizer

A method for receiving autonomous vehicle (AV) map data associated with an AV map of a geographic location and coverage map data associated with a coverage map of the geographic location. The AV map data is associated with an AV lane of a roadway in the geographic location, and the coverage map data is associated with a coverage lane of the roadway in the geographic location. The method includes generating a hybrid map of the geographic location based on the AV map data and the coverage map data and providing hybrid map data associated with the hybrid map for routing of an AV. The hybrid map includes the AV lane linked with the coverage lane of the roadway.

Map creation from hybrid data

Real time or near real time assessment of the quality of securities transactions is accomplished by intercepting order and execution communications between trader and broker, comparing the execution data with contemporaneous market data relative to the transaction or transactions involved, and informing the trader of that comparison. This is accomplished without interrupting or impeding the trader-broker communications, except that a broker-trader communication may, if desired, be interrupted and relevant data aggregated until the aggregate reaches a desired value. It is preferred that the comparison be between the volume-weighted average price of the securities transaction and the volume-weighted average price of the market data for that security, over the life of the order. A particular effective way to communicate the quality evaluation to the trader is disclosed.

Process for providing timely quality indication of market trades

A georeferenced trajectory estimation system receives data generated by vehicle sensors from a fleet of vehicles and identifies sets of sensor measurements for a geographic region taken at different times. The sets of sensor measurements include environment geometry data and unaligned trajectory data in local coordinate frames. The georeferenced trajectory estimation system aligns the environment geometry data between sets of sensor measures, and, based on the alignment of the environment geometry data, transforms the identified corresponding trajectory data into a common coordinate frame.

Georeferenced trajectory estimation system

A georeferenced trajectory estimation system for vehicles receives trajectory data generated by a plurality of vehicle sensors and fixed reference points of previously generated maps and aligns geometry data for a geographic region and trajectory data from the received data from different map builds. The trajectory data from respective map builds is aligned with fixed reference points of previously generated maps to generate a map of the geographic region. The received map data may include submap or spatially indexed data that is used to provide estimates of where a vehicle in an unmapped area is located by generating a series of pose estimates relating back to a fixed reference point in a previously mapped area. The resulting map expands the coverage of the existing map such that old and new map data is in a common consistent reference frame whereby the map may be built incrementally while ensuring global consistency.

Log trajectory estimation for globally consistent maps

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Pittsburgh, PA, United States of America

Peter Hansen