Traffic obstruction detection

• US 10,692,370 B2
• Filed: 02/27/2015
• Issued: 06/23/2020
• Est. Priority Date: 03/03/2014
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Elgie, Judith Rosalyn, Jordan, Dominic
• Primary Examiner(s): Cole, Brandon S
• Application Number:
  US15/122,750
• Publication Number:
  US 20170076227A1
• Time in Patent Office: 1,943 Days
• Field of Search: 706 1- 62
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• CPC Class Codes: A61B 5/4845 Toxicology, e.g. by detection of alcohol, drug or toxic products A61B5/4848 takes precedence
• CPC Class Codes: B60R 16/0236 for economical driving
• CPC Class Codes: B60W 2040/0809 Driver authorisation; Driver identity check
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2555/20 Ambient conditions, e.g. wind or rain
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2710/1044 Output speed change rate
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/08 related to drivers or passengers
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B64C 39/024 : of the remote controlled vehicle type, i.e. RPV
• CPC Class Codes: B64U 2101/30 : for imaging, photography or videography
• CPC Class Codes: G01C 21/3415 : Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3608 : using speech input, e.g. using speech recognition
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01C 21/3655 : Timing of guidance instructions
• CPC Class Codes: G01C 21/3667 : Display of a road map G01C21/3614 takes precedence; guidance using 3D or perspective road maps G01C21/3635
• CPC Class Codes: G01C 21/3682 : output of POI information on a road map G01C21/3614, G01C21/3685 take precedence
• CPC Class Codes: G05D 1/0011 : associated with a remote control arrangement
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06Q 20/102 : Bill distribution or payments
• CPC Class Codes: G06Q 2240/00 : Transportation facility access, e.g. fares, tolls or parking
• CPC Class Codes: G06Q 30/0283 : Price estimation or determination
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06Q 50/40 : Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07B 15/00 : Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points handling coins or valuable papers, e.g. banknotes, G07D; apparatus for vending or hiring articles or services activated by coins, credit cards, paper currency or the like G07F7/00, G07F17/00
• CPC Class Codes: G07B 15/063 : using wireless information transmission between the vehicle and a fixed station identification of vehicles G08G
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 : from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 : for classifying traffic situation
• CPC Class Codes: G08G 1/0141 : for traffic information dissemination
• CPC Class Codes: G08G 1/0145 : for active traffic flow control
• CPC Class Codes: G08G 1/065 : by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
• CPC Class Codes: G08G 1/07 : Controlling traffic signals
• CPC Class Codes: G08G 1/093 : Data selection, e.g. prioritizing information, managing message queues, selecting the information to be output
• CPC Class Codes: G08G 1/0962 : having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G08G 1/0965 : responding to signals from another vehicle, e.g. emergency vehicle
• CPC Class Codes: G08G 1/0967 : Systems involving transmission of highway information, e.g. weather, speed limits transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 : where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/096811 : where the route is computed offboard
• CPC Class Codes: G08G 1/096822 : where the segments of the route are transmitted to the vehicle at different locations and times
• CPC Class Codes: G08G 1/096838 : where the user preferences are taken into account or the user selects one route out of a plurality
• CPC Class Codes: G08G 1/097 : Supervising of traffic control systems, e.g. by giving an alarm if two crossing streets have green light simultaneously
• CPC Class Codes: H04B 1/3822 : specially adapted for use in vehicles H04B1/3827 takes precedence
• CPC Class Codes: H04B 7/18504 : Aircraft used as relay or high altitude atmospheric platform
• CPC Class Codes: H04L 67/02 : based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 9/3247 : involving digital signatures
• CPC Class Codes: H04M 15/60 : based on actual use of network resources
• CPC Class Codes: H04W 12/08 : Access security
• CPC Class Codes: H04W 4/024 : Guidance services
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]
• CPC Class Codes: H04W 4/42 : for mass transport vehicles, e.g. buses, trains or aircraft
• CPC Class Codes: H04W 4/48 : for in-vehicle communication
• CPC Class Codes: H04W 4/50 : Service provisioning or reconfiguring

Abstract

One or more techniques and/or systems are provided for training and/or utilizing a traffic obstruction identification model for identifying traffic obstructions based upon vehicle location point data. For example, a training dataset, comprising sample vehicle location points (e.g., global positioning system location points of vehicles) and traffic obstruction identification labels (e.g., locations of known traffic obstructions such as stop signs, crosswalks, stop lights, etc.), may be evaluated to extract a set of training features indicative of traffic flow patterns. The set of training features and the traffic obstruction identification labels may be used to train a traffic obstruction identification model to create a trained traffic obstruction identification model. The trained traffic obstruction identification model may be used to determine whether a road segment has a traffic obstruction or not.

Claims

1. A method for training a traffic obstruction identification model, comprising:

obtaining a training dataset comprising sample vehicle location points and traffic obstruction identification labels, wherein the traffic obstruction identification labels correspond to traffic obstructions configured to control a flow of traffic and disposed at fixed locations along road segments;

extracting a set of training features from the training dataset based upon the sample vehicle location points, the set of training features indicative of traffic flow patterns, the traffic flow patterns indicative of a flow of traffic along a first set of one or more road segments; and

training a traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create a trained traffic obstruction identification model for identifying other traffic obstructions along a second set of one or more road segments different than the first set of one or more road segments based upon traffic flow patterns of vehicles encountering the other traffic obstructions along the second set of one or more road segments, the training a traffic obstruction identification model comprising;

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a first traffic obstruction as being present on a first road segment of the second set of one or more road segments when a traffic flow pattern of the first road segment matches a traffic flow pattern of a second road segment of the first set of one or more road segments having the first traffic obstruction; and

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a second traffic obstruction as being present on a third road segment of the second set of one or more road segments when a traffic flow pattern of the third road segment matches a traffic flow pattern of a fourth road segment of the first set of one or more road segments having the second traffic obstruction.

2. The method of claim 1, the traffic obstructions comprising at least one of a stop light, a stop sign, a crosswalk, or a railroad crossing.

3. The method of claim 1, the extracting a set of training features comprising:

evaluating the sample vehicle location points to identify a count of vehicles having speeds below a speed threshold; and

comparing the count of vehicles to a total count of vehicles to determine a vehicle speed feature for inclusion within the set of training features.

4. The method of claim 1, the extracting a set of training features comprising:

evaluating the sample vehicle location points to determine a median speed; and

identifying a standard deviation from the median speed to determine a median average deviation feature for inclusion within the set of training features.

5. The method of claim 1, the extracting a set of training features comprising:

identifying a first count of vehicle location points within a fifth road segment; and

comparing the first count of vehicle location points to counts of vehicle location points within one or more neighboring road segments to determine a relative point density feature for inclusion within the set of training features.

6. The method of claim 1, the training a traffic obstruction identification model comprising:

identifying one or more parameters for use by the trained traffic obstruction identification model based upon the training dataset and the set of training features.

7. The method of claim 1, the extracting a set of training features comprising:

extracting a first set of training features for the second road segment of the first set of one or more road segments and associated with the first traffic obstruction of the traffic obstructions; and

extracting a second set of training features for the fourth road segment of the first set of one or more road segments and associated with the second traffic obstruction of the traffic obstructions, the second traffic obstruction different than the first traffic obstruction.

10. The method of claim 1, wherein the traffic flow patterns comprise a stopping pattern along at least one road segment of the first set of one or more road segments.

11. The method of claim 1, wherein the traffic flow patterns comprise an acceleration pattern along at least one road segment of the first set of one or more road segments.

12. The method of claim 1, wherein the traffic flow patterns comprise a speed pattern along at least one road segment of the first set of one or more road segments.

13. The method of claim 1, wherein the traffic flow patterns comprise a length of a queued line of vehicles along at least one road segment of the first set of one or more road segments.

8. A system for training a traffic obstruction identification model, comprising:

a model training component configured to;

obtain a training dataset comprising sample vehicle location points and traffic obstruction identification labels, wherein the traffic obstruction identification labels correspond to traffic obstructions configured to control a flow of traffic and disposed at fixed locations along road segments;

extract a set of training features from the training dataset based upon the sample vehicle location points, the set of training features indicative of traffic flow patterns, the traffic flow patterns indicative of a flow of traffic along a first set of one or more road segments; and

train a traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create a trained traffic obstruction identification model for identifying other traffic obstructions along a second set of one or more road segments different than the first set of one or more road segments based upon traffic flow patterns of vehicles encountering the other traffic obstructions along the second set of one or more road segments, wherein training the traffic obstruction identification model comprises;

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a first traffic obstruction as being present on a first road segment of the second set of one or more road segments when a traffic flow pattern of the first road segment matches a traffic flow pattern of a second road segment of the first set of one or more road segments having the first traffic obstruction; and

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a second traffic obstruction as being present on a third road segment of the second set of one or more road segments when a traffic flow pattern of the third road segment matches a traffic flow pattern of a fourth road segment of the first set of one or more road segments having the second traffic obstruction.

9. The system of claim 8, wherein the traffic flow patterns comprise at least one of:

a stopping pattern along at least one road segment of the first set of one or more road segments,

an acceleration pattern along the at least one road segment of the first set of one or more road segments,

a speed pattern along the at least one road segment of the first set of one or more road segments, or

a length of a queued line of vehicles along the at least one road segment of the first set of one or more road segments.

14. A computer readable medium comprising instructions which when executed perform a method for training a traffic obstruction identification model, comprising:

obtaining a training dataset comprising sample vehicle location points and traffic obstruction identification labels, wherein the traffic obstruction identification labels correspond to traffic obstructions configured to control a flow of traffic and disposed at fixed locations along road segments;

extracting a set of training features from the training dataset based upon the sample vehicle location points, the set of training features indicative of traffic flow patterns, the traffic flow patterns indicative of a flow of traffic along a first set of one or more road segments; and

training a traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create a trained traffic obstruction identification model for identifying other traffic obstructions along a second set of one or more road segments different than the first set of one or more road segments based upon traffic flow patterns of vehicles encountering the other traffic obstructions along the second set of one or more road segments, the training a traffic obstruction identification model comprising;

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a first traffic obstruction as being present on a first road segment of the second set of one or more road segments when a traffic flow pattern of the first road segment matches a traffic flow pattern of a second road segment of the first set of one or more road segments having the first traffic obstruction; and

training the traffic obstruction identification model using the set of training features and the traffic obstruction identification labels to create the trained traffic obstruction identification model to identify a second traffic obstruction as being present on a third road segment of the second set of one or more road segments when the traffic flow pattern of a third road segment matches a traffic flow pattern of a fourth road segment of the first set of one or more road segments having the second traffic obstruction.

15. The computer readable medium of claim 14, the traffic obstructions comprising at least one of a stop light, a stop sign, a crosswalk, or a railroad crossing.

16. The computer readable medium of claim 14, the extracting a set of training features comprising:

evaluating the sample vehicle location points to identify a count of vehicles having speeds below a speed threshold; and

comparing the count of vehicles to a total count of vehicles to determine a vehicle speed feature for inclusion within the set of training features.

17. The computer readable medium of claim 14, the extracting a set of training features comprising:

evaluating the sample vehicle location points to determine a median speed; and

identifying a standard deviation from the median speed to determine a median average deviation feature for inclusion within the set of training features.

18. The computer readable medium of claim 14, the extracting a set of training features comprising:

identifying a first count of vehicle location points within a fifth road segment; and

comparing the first count of vehicle location points to counts of vehicle location points within one or more neighboring road segments to determine a relative point density feature for inclusion within the set of training features.

19. The computer readable medium of claim 14, the training a traffic obstruction identification model comprising:

identifying one or more parameters for use by the trained traffic obstruction identification model based upon the training dataset and the set of training features.

20. The computer readable medium of claim 14, the extracting a set of training features comprising:

extracting a first set of training features for the second road segment of the first set of one or more road segments and associated with the first traffic obstruction of the traffic obstructions; and

extracting a second set of training features for the fourth road segment of the first set of one or more road segments and associated with the second traffic obstruction of the traffic obstructions, the second traffic obstruction different than the first traffic obstruction.