Rapid traffic parameter estimation

Patent metadata

Field	Value
Publication number	US-10495469-B2
Application number	US-201514747187-A
Country	US
Kind code	B2
Filing date	Jun 23, 2015
Priority date	Jun 23, 2015
Publication date	Dec 3, 2019
Grant date	Dec 3, 2019

Abstract

Data about vehicle movement at a stoplight are collected. A stoplight cycle time is predicted with a probability model. The data are compared to the predicted stoplight cycle time. A noise function is applied to the data to generate noise-applied data. The probability model for the predicted stoplight cycle time is updated by scaling the probability model with the noise-applied data to generate a new probability model. A recommended vehicle operation is provided via a network to at least one vehicle computer based on the predicted stoplight cycle time determined by the new probability model.

First claim

What is claimed is:

1. A system, comprising a computer including a processor and a memory, the memory storing instructions executable by the computer to:
collect data about vehicle movement at a stoplight;
determine a time of a start of motion of a vehicle at the stoplight from the collected data;
predict a first stoplight cycle time with a first probability model of the stoplight cycle time based at least in part on the time of the start of motion of the vehicle, the probability model indicating a first range of probabilities of the first predicted stoplight cycle time;
compare the collected data to the predicted stoplight cycle time to generate a second probability model, based on the first probability model, that specifies a second range of probabilities of a second predicted stoplight cycle time;
apply a noise function to the collected data to generate noise-applied data;
update the second probability model for the predicted stoplight cycle time by adjusting a set of probabilities of the second probability model with the noise-applied data to generate an updated second probability model;
predict the second predicted stoplight cycle time based on the adjusted set of probabilities;
predict an amount of time spent at a green light during the second predicted stoplight cycle time based on the second predicted stoplight cycle time;
predict a duty cycle for the stoplight that is a ratio of the predicted amount of time spent at the green light to the second predicted stoplight cycle time; and
provide, via a network to at least one vehicle computer, a recommended vehicle operation based, at least in part, on the duty cycle.

2. The system of claim 1 , wherein the recommended vehicle operation includes recommended driving patterns for reduced fuel usage.

3. The system of claim 2 , wherein the recommended driving pattern includes a recommended operating speed determined so that a vehicle reaches the stoplight during a specified portion during the stoplight cycle time.

4. The system of claim 3 , wherein the recommended driving pattern includes a route to a destination determined so that the vehicle reaches the stoplight at specified portions of the stoplight cycle time.

5. The system of claim 1 , wherein the recommended vehicle operation is determined, at least in part, by the current time.

6. The system of claim 1 , wherein the instructions further include instructions to send a notification with the recommended vehicle operation to a handheld user device.

7. The system of claim 1 , wherein the instructions further include instructions to determine an offset for the stoplight based on the stoplight cycle time.

8. The system of claim 1 , further comprising a data collector to collect the data on vehicle movement timing.

9. The system of claim 1 , wherein the stoplight cycle time is determined, at least in part, by the vehicle movement timing data.

10. The system of claim 1 , wherein the probability model is one of a plurality of probability models, each probability model being configured to predict the stoplight time for a specific time range during a day.

11. The system of claim 10 , wherein the instructions further include instructions to, if after comparing the data to the predicted stoplight cycle time, the data are not within a threshold of the predicted stoplight time, select the next of the plurality of probability models, predict the stoplight cycle time with the probability model, and compare the data to the new predicted stoplight cycle time.

12. The system of claim 11 , wherein the instructions further include instructions to, if none of the plurality of probability models generate a predicted stoplight time for which the data fall within the threshold, generate a new probability model and append to the plurality of probability models.

13. A method, comprising:
collecting data about vehicle movement at a stoplight;
determining a time of a start of motion of a vehicle at the stoplight from the collected data;
predicting a first stoplight cycle time with a first probability model of the stoplight cycle time based at least in part on the time of the start of motion of the vehicle, the probability model indicating a first range of probabilities of the first predicted stoplight cycle time;
comparing the collected data to the predicted stoplight cycle time to generate a second probability model, based on the first probability model, that specifies a second range of probabilities of a second predicted stoplight cycle time;
applying a noise function to the collected data to generate noise-applied data;
updating the second probability model for the predicted stoplight cycle time by adjusting a set of probabilities of the second probability model with the noise-applied data to generate an updated second probability model;
predicting the second predicted stoplight cycle time based on the adjusted set of probabilities;
predicting an amount of time spent at a green light during the second predicted stoplight cycle time based on the second predicted stoplight cycle time;
predicting a duty cycle for the stoplight that is a ratio of the predicted amount of time spent at the green light to the second predicted stoplight cycle time; and
providing, via a network to at least one vehicle computer, a recommended vehicle operation based, at least in part, on the duty cycle.

14. The method of claim 13 , wherein the recommended vehicle operation includes recommended driving patterns for reduced fuel usage.

15. The method of claim 14 , wherein the recommended driving pattern includes a recommended operating speed determined so that a vehicle reaches the stoplight during a specified part of the stoplight cycle time.

16. The method of claim 15 , wherein the recommended driving pattern includes a route to a destination determined so that the vehicle reaches the stoplights at specified parts of the stoplight cycle time.

17. The method of claim 13 , further comprising determining an offset for the stoplight based on the stoplight cycle time.

18. The method of claim 13 , wherein the probability model is one of a plurality of probability models, each probability model being configured to predict the stoplight time for a specific time range during a day.

19. The method of claim 13 , wherein the stoplight cycle time is determined, at least in part, by the vehicle movement timing data.

20. The method of claim 13 , wherein the probability model is one of a plurality of probability models, each probability model being configured to predict the stoplight time for a specific time range during a day.

Assignees

• Ford Global Tech Llc

Inventors

• Ozatay Engin
• Filev Dimitar Petrov
• Michelini John Ottavio

Classifications

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G08G1/0962

  having an indicator mounted inside the vehicle, e.g. giving voice messages · CPC title

• G06Q50/26

  Government or public services (business processes related to the transportation industry G06Q50/40) · CPC title

• G08G1/0968 Primary

  Systems involving transmission of navigation instructions to the vehicle · CPC title

• G08G1/0104

  Measuring and analyzing of parameters relative to traffic conditions · CPC title