Lyft, Inc.

Improbable Worlds Ltd

Michal Witkowski

Matthew John Reveley Lewis

Lorenzo Peppoloni

Charles Micou

Joan Devassy

Rok Strnisa

Filippo Brizzi

Swarn Avinash Kumar

Hugo Oscar Bonay Grimmett

Mousom Dhar Gupta

Examples disclosed herein may involve a computing system that is operable to (i) derive, from a first set of one or more images captured by a monocular camera associated with a vehicle, a first set of position information for a given agent; (ii) derive, from a second set of one or more image pairs captured by a stereo camera associated with the vehicle, a second set of position information for the given agent; (iii) input the first and second sets of position information for the given agent into a motion model that encodes knowledge regarding physical constraints on the given agent's real-world behavior; and (iv) determine a trajectory for the given agent based on an output of the motion model.

Systems and methods for deriving an agent trajectory based on multiple image sources

Examples disclosed herein involve a computing system configured to (i) obtain (a) a first set of sensor data captured by a first sensor system of a first vehicle that indicates the first vehicle's movement and location with a first degree of accuracy and (b) a second set of sensor data captured by a second sensor system of a second vehicle that indicates the second vehicle's movement and location with a second degree of accuracy that differs from the first degree of accuracy, (ii) based on the first set of sensor data, derive a first trajectory for the first vehicle that is defined in terms of a source-agnostic coordinate frame, (iii) based on the second set of sensor data, derive a second trajectory for the second vehicle that is defined in terms of the source-agnostic coordinate frame, and (iv) store the first and second trajectories in a database of source-agnostic trajectories.

Systems and methods for generating source-agnostic trajectories

Examples disclosed herein may involve a computing system that is operable to (i) derive a first representation of an agent's trajectory from a first set of sensor data captured by a first sensor system associated with a vehicle, (ii) derive a second representation of the agent's trajectory from a second set of sensor data captured by a second sensor system associated with the vehicle, (iii) align the spatial reference frames of the first and second representations by applying a spatial transformation to a given one of the first and second representations, and (iv) align the temporal reference frames of the first and second representations by determining an origin-time offset between the temporal reference frames of the first and second representations and applying the determined origin-time offset to timing information encoded in the given one of the first and second representations.

Systems and methods for aligning trajectory information derived from different source data

Examples disclosed herein may involve a computing system that is operable to (i) receive a first sequence of images captured by a monocular camera associated with a vehicle during a given period of operation and a second sequence of image pairs captured by a stereo camera associated with the vehicle during the given period of operation, (ii) derive, from the first sequence of images captured by the monocular camera, a first track for a given agent that comprises a first sequence of position information for the given agent, (iii) derive, from the second sequence of image pairs captured by the stereo camera, a second track for the given agent that comprises a second sequence of position information for the given agent, and (iv) determine a trajectory for the given agent based on the first and second tracks for the given agent.

Examples disclosed herein may involve a computing system that is operable to (i) receive a sequence of images captured by a camera associated with a vehicle, (ii) for each of at least a subset of the received images in which a given agent is detected, (a) generate a respective pixel mask that identifies a boundary of the given agent within the image, (b) identify, as a tracking point for the given agent within the image, at least one given pixel within the pixel mask that is representative of an estimated intersection point between the given agent and a ground plane, and (c) determine a position of the given agent at the capture time of the image based on the tracking point and information regarding the ground plane, and (iii) determine a trajectory for the given agent based on the determined positions of the given agent.

Systems and methods for deriving an agent trajectory based on tracking points within images

Methods, systems, computer-readable media, and apparatuses for determining partitions and virtual processes in a simulation are presented. A plurality of partitions of a simulated world may be determined, and each partition may correspond to a different metric for entities in the simulated world. A plurality of virtual processes for the simulated world may also be determined. The system may assign a different virtual process to each partition. An indication of the partitions may be sent to one or more partition enforcer services, and an indication of the virtual processes may be sent to a virtual process manager.

Distributable and customizable load-balancing of data-associated computation via partitions and virtual processes

Methods, systems, computer-readable media, and apparatuses for performing, providing, managing, executing, and/or running a distributed simulation are presented. In one or more embodiments, the distributed simulation may comprise a plurality of workers performing the simulation, and workers may send commands to other workers authoritative over entity components. A mapping of entity components to workers may be used to determine a bridge associated with a worker to which to send a command. A request to invoke the command may be transmitted to the worker via the bridge associated with the worker. The worker transmitting the command request may receive a response to the request to invoke the command, such as a success response or a failure response.

Commands for simulation systems and methods

Methods, systems, computer-readable media, and apparatuses for grouping bridges in a simulation are presented. In some examples, grouping bridges may result in more efficient usage of data connections in a simulation and less duplicative data being sent during the simulation. The simulation may be performed by receiving an indication of a streaming query for each worker of a plurality of workers in a worker layer. A plurality of bridges in a bridge layer may be run, and the plurality of bridges may be configured to facilitate data communications between the plurality of workers in the worker layer and one or more databases in a database layer. Each worker of the plurality of workers may be assigned to a different bridge of the plurality of bridges. Based on the streaming query for each worker, the plurality of bridges may be grouped into different groups of bridges.

Growing community of inventors

London, United Kingdom

Michal Witkowski