Inferring a behavioral state of a vehicle

• US 8,543,320 B2
• Filed: 05/19/2011
• Issued: 09/24/2013
• Est. Priority Date: 05/19/2011
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Zheng, Yu, Xie, Xing
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): Lee, Tyler J
• Application Number:
  US13/111,110
• Publication Number:
  US 20120296560A1
• Time in Patent Office: 859 Days
• Field of Search: 701/117, 701/118, 701/119, 340989-994, 705/14.63, 705/28
• US Class Current: 701/119
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data

Abstract

Trajectory data representing tracked positions of a vehicle along a trajectory having a start and end point is accessed. The trajectory data may include spatio-temporal information about the vehicle at different points along the trajectory. The trajectory may be divided into segments based, at least in part, on knowledge of inferred-parking locations. The segments may be map-matched to corresponding road segments. Additionally, historical data representing spatio-temporal travel patterns of vehicles learned from historical trajectories of vehicles corresponding to the map-matched-road segments may also be accessed. A behavioral state of the vehicle for a segment or position within a segment may be inferred, based at least in part, on (i) the vehicle'"'"'s spatio-temporal information corresponding to the segment or position within a segment, (ii) knowledge of the map-matched-road segment, and (iii) the historical data.

Claims

1. A computer-implemented method, comprising:

under control of one or more computer systems containing memory and configured with executable instructions stored in the memory,

accessing from the memory trajectory data representing tracked positions of a vehicle along a trajectory having a start point and an end point, the trajectory data including recorded times, and speed of the vehicle at different points along the trajectory;

identifying one or more potential parking locations of the vehicle along the trajectory;

dividing the trajectory into segments based, at least in part, on knowledge of the identified potential parking locations;

map-matching the segments to corresponding road segments;

accessing historical data stored in the memory representing spatio-temporal travel patterns of vehicles learned from historical trajectories of vehicles corresponding to the map-matched road segments;

automatically inferring, by the one or more computer systems, a behavioral state of the vehicle for a segment or position within a segment, based at least in part, on a position, time of day, and speed of the vehicle corresponding to the segment or position within the segment, knowledge of the map-matched-road segment, and the historical data;

repeating the accessing, identifying, map-matching, and inferring operations for a plurality of vehicles to identify trajectory data for the plurality of vehicles; and

analyzing the trajectory data for the plurality of vehicles to attempt to identify a pattern, the analysis including detecting whether passengers of the plurality of vehicles travel between a first and second region at a frequency that is greater than a predefined threshold.

2. The method of claim 1, wherein the behavioral state of the vehicle includes at least one of:

an occupied state in which the vehicle is transporting at least one passenger, or a non-occupied state in which the vehicle is traveling without a passenger.

3. The method of claim 1, wherein identifying the one or more potential parking locations includes detecting when a speed and distance between one or more consecutive points along the trajectory is less than a predefined threshold.

4. The method of claim 1, wherein map-matching the segments to corresponding road segments includes assigning an attribute associated with a road segment to the road segment.

5. The method of claim 1, wherein map-matching the segments to corresponding road segments includes assigning an attribute associated with a road segment to the road segment, wherein the attribute includes metadata indicative of (i) whether the road segment is a highway, a one-way road, a two-way road, a single-lane road, a multiple-lane road;

(ii) a length of the road segment, and/or (iii) a point-of-interest along the road segment.

6. The method of claim 1, wherein the behavioral state of the vehicle is further automatically inferred based at least in part on past behavior patterns of passengers of vehicles according to the historical data.

7. The method of claim 1, wherein accessing the historical data further includes mining a prior probability that the vehicle will be in a certain behavioral state corresponding to a map-matched-road segment.

8. The method of claim 1, wherein accessing the historical data further includes mining a prior probability that the vehicle will be in a certain behavioral state based on the spatial-temporal relationship of the vehicle corresponding to a map-matched-road segment.

9. The method of claim 1, wherein the accessing of the historical further includes accessing prior probabilities that the vehicle transitions from (i) a first-behavioral state, in which the vehicle is occupied by a passenger, to (ii) a second-behavioral state, in which the vehicle is unoccupied by a passenger, and vice versa.

10. The method of claim 1, further comprising tracking the vehicle'"'"'s position from a receiver mounted on or in the vehicle.

11. The method of claim 1, wherein the vehicle is a service vehicle for transporting passengers.

12. The method of claim 1, wherein the vehicle is a taxi.

13. The method of claim 1, further comprising analyzing whether a pattern is detected, the analysis including detecting whether roads connecting two locations are heavy with frequent traffic jams at specific times of day.

14. One or more computer storage media encoded with computer-executable instructions that, when executed, configure a computer system to perform a method as recited in claim 1.

15. A computer-implemented method, comprising:

under control of one or more computer systems containing memory and configured with executable instructions stored in the memory,

determining a trajectory of a vehicle based at least in part on trajectory data representing tracked positions of the vehicle, the trajectory data including recorded times, and speed of the vehicle at the positions along the trajectory;

accessing previously-recorded traffic patterns of vehicles along the trajectory; and

inferring, by the one or more computer systems, a state of the vehicle based at least in part on the trajectory and the previously-recorded traffic patterns, the state of the vehicle including at least one of;

an occupied state, in which the vehicle is transporting a passenger, and a non-occupied state, in which the vehicle is traveling without a passenger.

16. The method of claim 15, further comprising selecting trajectory data associated with the vehicle when in an occupied state in lieu of trajectory data associated with the vehicle when in a non-occupied state.

20. The method of claim 15, further comprising:

repeating the determining, accessing, and inferring operations for a fleet of vehicles to identify trajectory data for the fleet; and

identifying a pattern from the trajectory data for the fleet by detecting whether passengers of vehicles of the fleet travel between a first and second region at a frequency that is greater than a predefined threshold.

17. A system, comprising:

one or more processors;

a memory communicatively coupled to the one or more processors; and

an application at least partially stored in the memory and executable on the one or more processors, the application including;

a first module configured to process a trajectory of a vehicle based at least in part on a tracked position of the vehicle, the tracked position of the vehicle including spatial-temporal data;

the first module further configured to identify one or more potential parking locations of the vehicle along the trajectory and divide the trajectory into segments based, at least in part, on knowledge of the identified potential parking locations;

a second module configured to map-match attributes from the segments to corresponding road segments;

a third module configured to access historical data representative of previously-recorded traffic patterns of vehicles along the trajectory;

wherein the first, second, and third, modules form a collective module configured to infer a state of the vehicle based on the spatio-temporal data associated with the trajectory, map-matched attributes, and the historical data, the state of the vehicle including at least one of;

an occupied state in which the vehicle is transporting at least one passenger, and a non-occupied state in which the vehicle is traveling without a passenger.

18. The system of claim 17, wherein the application is further configured to select trajectory data associated with the vehicle when in an occupied state in lieu of trajectory data associated with the vehicle when in a non-occupied state.

19. The system of claim 17, further comprising one or more modules configured to:

repeat the processing, map-matching, accessing, and inferring operations for a fleet of vehicles to identify trajectory data for the fleet; and

identify a pattern from the trajectory data for the fleet by detecting whether passengers of vehicles of the fleet travel between a first and second region at a frequency that is greater than a predefined threshold.