Systems and methods for estimating traffic signal information

• US 9,697,729 B2
• Filed: 10/05/2015
• Issued: 07/04/2017
• Est. Priority Date: 10/31/2013
• Status: Active Grant

• Current Assignee: Bayerische Motoren Werke AG
• Original Assignee: Bayerische Motoren Werke AG
• Inventors: Fayazi, S. Alireza, Mahler, Grant, Vahidi, Ardalan, Winckler, Andreas
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): TESTARDI, DAVID A
• Application Number:
  US14/875,440
• Publication Number:
  US 20160098924A1
• Time in Patent Office: 638 Days
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination

Abstract

Traffic signal information is estimated based on positioning system data obtained from a plurality of vehicles. Each data set includes the position and the velocity of a vehicle as functions of time. For an intersection having a traffic signal, an average acceleration of the vehicles when leaving the intersection is estimated, and an average deceleration of the vehicles when approaching the intersection is estimated. For each of a subset of the vehicles, a stop duration at the intersection is estimated based on the average acceleration, the average deceleration, and the positioning system data for the respective vehicle. A duration of a red phase of the traffic signal is estimated based on the stop duration of each of the subset of the vehicles.

Claims

1. A method of estimating traffic signal information and adjusting an operation of an on-board system of a vehicle, the method comprising:

obtaining positioning system data from each of a plurality of vehicles, wherein said positioning system data for each of the plurality of vehicles comprises position and velocity of a vehicle as functions of time;

for an intersection having a traffic signal, estimating an average acceleration of the vehicles when leaving the intersection;

for each of a subset of the vehicles, estimating a start time at which the respective vehicle leaves the intersection based on the positioning system data for the respective vehicle and the average acceleration;

wherein the subset of vehicle is determined by;

selecting vehicles whose positioning system data include data points within a distance interval surrounding the intersection, wherein at least a first one of the data points is before the intersection and at least a second one of the data points is after the intersection,

removing vehicles from the subset whose velocity is lower, at a point either before the intersection or after the intersection, than a threshold;

estimating a cycle time of the traffic signal by;

for each pair of consecutive vehicles in the subset, calculating a difference between the start times of the vehicles, and

solving an optimization problem based on the differences and the cycle time; and

adjusting the operation of the on-board system of the vehicle based, at least in part, on an estimated start of a future green phase of the traffic signal.

2. The method according to claim 1, further comprising:

fitting a Gaussian mixture model to a histogram of a remainder of a division of the difference and the cycle time; and

estimating a signal offset caused by a schedule change of the traffic signal based on clusters within the Gaussian mixture model.

3. The method according to claim 1, wherein the average acceleration is estimated based on the positioning system data for the vehicles and a location of a stop bar behind which the vehicles stop at the intersection.

4. The method according to claim 3, wherein estimating the average acceleration comprises using a least-square estimation based on data points of velocity versus traveled distance corresponding to the vehicles and by removing outliers of said data points.

5. The method according to claim 1, wherein estimating the average acceleration comprises using a least-square estimation based on data points of velocity versus traveled distance corresponding to the vehicles and by removing outliers of said data points.

6. The method according to claim 1, wherein the subset of the vehicles is further determined by:

for each of the remaining vehicles after said removing, estimating an intersection delay based on the positioning system data for the respective vehicle; and

removing vehicles whose estimated intersection delay is negative or zero.

7. The method according to claim 1, wherein each data set is obtained from at least one of a cellular telephone or a navigation device within the vehicle.

8. The method according to claim 1, wherein an update frequency of the positioning system data is not greater than twice per minute.

9. A method of estimating traffic signal information and adjusting an operation of an on-board system of a vehicle, the method comprising:

obtaining positioning system data from each of a plurality of vehicles, wherein said positioning system data for each of the plurality of vehicles comprises position and velocity of a vehicle as functions of time;

for an intersection having a traffic signal, estimating an average acceleration of the vehicles when leaving the intersection;

for each of a subset of the vehicles, estimating a start time at which the respective vehicle leaves the intersection based on the positioning system data for the respective vehicle and the average acceleration;

estimating a cycle time of the traffic signal by;

for each pair of consecutive vehicles in the subset, calculating a difference between the start times of the vehicles, and

solving an optimization problem based on the differences and the cycle time;

adjusting the operation of the on-board system of the vehicle based, at least in part, on an estimated start of a future green phase of the traffic signal;

fitting a Gaussian mixture model to a histogram of a remainder of a division of the difference and the cycle time; and

estimating a signal offset caused by a schedule change of the traffic signal based on clusters within the Gaussian mixture model.

10. The method according to claim 9, wherein the average acceleration is estimated based on the positioning system data for the vehicles and a location of a stop bar behind which the vehicles stop at the intersection.

11. The method according to claim 9, wherein estimating the average acceleration comprises using a least-square estimation based on data points of velocity versus traveled distance corresponding to the vehicles and by removing outliers of said data points.

12. The method according to claim 9, wherein the subset of the vehicles is determined by:

selecting vehicles whose positioning system data include data points within a distance interval surrounding the intersection, wherein at least a first one of the data points is before the intersection and at least a second one of the data points is after the intersection;

removing vehicles whose velocity is lower, at a point either before the intersection or after the intersection, than a threshold;

for each of the remaining vehicles, estimating an intersection delay based on the positioning system data for the respective vehicle; and

removing vehicles whose estimated intersection delay is negative or zero.

13. The method according to claim 9, wherein each data set is obtained from at least one of a cellular telephone or a navigation device within the vehicle.

14. The method according to claim 9, wherein an update frequency of the positioning system data is not greater than twice per minute.