Lyft, Inc.

Filippo Brizzi

Lorenzo Peppoloni

Luca Del Pero

Michal Witkowski

Tayyab Naseer

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.

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London, United Kingdom

Filippo Brizzi