Prediction variance estimation

Patent metadata

Field	Value
Publication number	US-12509073-B2
Application number	US-202318241041-A
Country	US
Kind code	B2
Filing date	Aug 31, 2023
Priority date	Aug 31, 2023
Publication date	Dec 30, 2025
Grant date	Dec 30, 2025

Abstract

Observed driveline mean and variance data are used for determining the variance of a trajectory of tracked objects for use by a host vehicle. A map of a portion of a vehicle transportation network are determined, wherein the map is comprised of observed driveline mean and variance data for one or more map points. At least one trajectory of a tracked object is predicted, wherein a trajectory includes a series of location each corresponding to a respective predicted position of the tracked object at a future time. A map-based variance is generated for the location of the trajectory using a smoothed curvature of the trajectory within the map. A control system of the vehicle operates the vehicle using the map-based variance as input.

First claim

What is claimed is:

1 . A method, comprising:
determining a map of a portion of a vehicle transportation network, wherein the map comprises observed driveline mean and variance data for one or more map points; predicting at least one trajectory for a tracked object, wherein a trajectory of the at least one trajectory includes a series of locations, a location of the series of locations corresponding to a respective predicted position of the tracked object at a future time; generating, for each location of the trajectory, a map-based variance based on a smoothed curvature of the trajectory and the observed driveline mean and variance data for the one or more map points; and operating a vehicle using the map-based variance as input to a control system of the vehicle.

2 . The method of claim 1 , comprising:
updating the map-based variance of the location in response to a map point of the one or more map points corresponding to the location.

3 . The method of claim 1 , wherein the map-based variance increases to a maximum variance in response to the location corresponding to a future time greater than a maximum time threshold.

4 . The method of claim 1 , comprising:
generating, for the map-based variance, a dynamic variance in response to a change in conditions within the vehicle transportation network as the tracked object follows the trajectory, wherein the dynamic variance shifts the map-based variance laterally.

5 . The method of claim 4 , comprising:
increasing the map-based variance, for a location, to a maximum variance, wherein the location corresponds to a future time greater than a maximum time threshold.

6 . The method of claim 5 , wherein the maximum variance is between 0 and 1 and the maximum time threshold is between 2 and 3 seconds.

7 . The method of claim 1 , wherein the at least one trajectory for the tracked object is based on an in-lane assessment.

8 . The method of claim 1 , wherein the at least one trajectory for the tracked object is based on an inferred relationship with nearby tracked objects.

9 . The method of claim 1 , wherein the map-based variances comprise a left variance and a right variance.

10 . The method of claim 1 , wherein generating the map-based variance comprises:
determining a previous curvature and a current curvature for the trajectory; calculating the smoothed curvature based on the previous curvature and the current curvature; and calculating the map-based variance based on the smoothed curvature, a variance factor, and a fixed variance.

11 . An apparatus, comprising:
a memory; and a processor configured to execute instructions stored in the memory to:
determine a map of a portion of a vehicle transportation network, wherein the map comprises observed driveline mean and variance data for one or more map points;
predict at least one trajectory for a tracked object, wherein a trajectory of the at least one trajectory includes a series of locations, a location of the series of locations corresponding to a respective predicted position of the tracked object at a future time;
generate, for each location of the trajectory, a map-based variance based on a smoothed curvature of the trajectory and the observed driveline mean and variance data for the one or more map points;
increase the map-based variance, for a location, to a maximum variance, wherein the location corresponds to a future time greater than a maximum time threshold; and
operate a vehicle using the map-based variance as input to a control system of the vehicle.

12 . The apparatus of claim 11 , wherein the processor is configured to execute instructions stored in the memory to:
update the map-based variance of the location in response to a map point of the one or more map points corresponding to the location.

13 . The apparatus of claim 11 , wherein the map-based variance increases to a maximum variance in response to the location corresponding to a future time greater than a maximum time threshold.

14 . The apparatus of claim 11 , wherein the processor is configured to execute instructions stored in the memory to:
generate, for the map-based variance, a dynamic variance in response to a change in a condition within the vehicle transportation network as the tracked object follows the trajectory, wherein the dynamic variance shifts the map-based variance laterally.

15 . The apparatus of claim 11 , wherein generating the map-based variance comprises:
determining a previous curvature and a current curvature for the trajectory; calculating the smoothed curvature based on the previous curvature and the current curvature; and calculating the map-based variance based on the smoothed curvature, a variance factor, and a fixed variance.

16 . A non-transitory computer-readable medium storing instructions operable to cause one or more processors to perform operations comprising:
determining a map of a portion of a vehicle transportation network, wherein the map comprises observed driveline mean and variance data for one or more map points; predicting at least one trajectory for a tracked object, wherein a trajectory of the at least one trajectory includes a series of locations, a location of the series of locations corresponding to a respective predicted position of the tracked object at a future time; determining a previous curvature and a current curvature for the trajectory; calculating a smoothed curvature based on the previous curvature and the current curvature; generating, for each location of the trajectory, a map-based variance based on the smoothed curvature, a variance factor, a fixed variance, and the observed driveline mean and variance data for the one or more map points; and operating a vehicle using the map-based variance as input to a control system of the vehicle.

17 . The non-transitory computer-readable medium storing instructions of claim 16 , the operations comprising:
generating, for the map-based variance, a dynamic variance in response to a change in a condition within the vehicle transportation network as the tracked object follows the trajectory, wherein the dynamic variance shifts the map-based variance laterally.

18 . The non-transitory computer-readable medium storing instructions of claim 17 , the operations comprising:
increasing the map-based variance, for a location, to a maximum variance, wherein the location corresponds to a future time greater than a maximum time threshold.

19 . The apparatus of claim 11 , wherein the maximum variance is between 0 and 1 and the maximum time threshold is between 2 and 3 seconds.

20 . The non-transitory computer-readable medium storing instructions of claim 16 , wherein the map-based variances comprise a left variance and a right variance.

Assignees

• Nissan North America Inc

Inventors

• Huynh Manh
• Ostafew Christopher
• Tam Qizhan
• Chen Huiching

Classifications

• B60W60/0027

  using trajectory prediction for other traffic participants · CPC title

• B60W2554/4044

  Direction of movement, e.g. backwards · CPC title

• B60W2556/40

  High definition maps · CPC title

• G01C21/3841

  Data obtained from two or more sources, e.g. probe vehicles · CPC title

• B60W30/0956 Primary

  the prediction being responsive to traffic or environmental parameters · CPC title