Lyft, Inc.

Lorenzo Peppoloni

Michal Witkowski

Filippo Brizzi

Luca Del Pero

Tayyab Naseer

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 may involve a computing system that is configured to (i) identify a set of trajectories traveled through a path within an area, (ii) for each of two or more sampling positions along the path, (a) determine a respective set of intersection points between the identified set of trajectories and the sampling position and (b) generate a respective aggregation of the respective set of intersection points, and (iii) based on the generated aggregations for the two or more sampling positions along the path, derive a path-prior dataset for the path.

Systems and methods for deriving path-prior data using collected trajectories

Examples disclosed herein involve a computing system configured to (i) based on image data captured by a vehicle in an environment, obtain observations of a time-sequence of positions of an agent identified within the image data, (ii) generate a first updated time-sequence of positions of the agent by performing a first optimization operation that includes processing the observed time-sequence of positions by beginning with a position associated with an observation having the highest confidence in the time-sequence of observations and proceeding in a first direction, (iii) after generating the first updated time-sequence of positions, generate a second updated time-sequence of positions of the agent by performing a second optimization operation that includes processing the first updated time-sequence of positions in a second direction opposite the first direction, and (iv) derive a trajectory for the agent in the environment based on the second updated time-sequence of positions for the agent.

Offline optimization of sensor data for agent 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.

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London, United Kingdom

Lorenzo Peppoloni