Abstract: Examples disclosed herein may involve a computing system that is operable to (i) use a first approach to produce a first representation of an agent's trajectory from a first set of sensor data, (ii) use a second approach to produce a second representation of the agent's trajectory from a second set of sensor data, wherein the first and second representations of the agent's trajectory are based on different spatial reference frames and different temporal reference frames, (iii) align the spatial reference frames of the first and second representations by applying a spatial transformation to one of the first or second representations, (iv) align the temporal reference frames by applying an origin-time offset to one of the first or second representations, and (v) use the aligned first and second representations as a basis for evaluating an accuracy of the first approach relative to the second approach.

Abstract: Examples disclosed herein may involve a computing system associated with a vehicle that is configured to (1) receive a path-prior dataset for a path within a real-world area, wherein the path-prior dataset is derived based on an evaluation of intersection points between (i) prior vehicle trajectories and (ii) segmentation lines that extend across the path and (2) determine a planned trajectory for the vehicle based at least in part on the path-prior dataset, wherein the planned trajectory is utilized to control a physical behavior of the vehicle.

Abstract: 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.

Abstract: 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.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) use a first approach to produce a first representation of an agent's trajectory from a first set of sensor data, (ii) use a second approach to produce a second representation of the agent's trajectory from a second set of sensor data, wherein the first and second representations of the agent's trajectory are based on different spatial reference frames and different temporal reference frames, (iii) align the spatial reference frames of the first and second representations by applying a spatial transformation to one of the first or second representations, (iv) align the temporal reference frames by applying an origin-time offset to one of the first or second representations, and (v) use the aligned first and second representations as a basis for evaluating an accuracy of the first approach relative to the second approach.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.