Turn predictions

• US 9,784,592 B2
• Filed: 07/17/2015
• Issued: 10/10/2017
• Est. Priority Date: 07/17/2015
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Gupta, Rakesh, Tang, Bo, Khokhar, Salman Anwar
• Primary Examiner(s): Jen, Ian
• Application Number:
  US14/801,904
• Publication Number:
  US 20170016734A1
• Time in Patent Office: 816 Days
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence

Abstract

One or more embodiments of techniques or systems for generating turn predictions or predictions are provided herein. Environment layout information of an operating environment through which a first vehicle is travelling may be received. A current location of the first vehicle may be received. One or more other vehicles may be detected. Additional environment layout information from other vehicles may be received. A model including the operating environment, the first vehicle, and one or more of the other vehicles may be built. The model may be based on the environment layout information and the additional environment layout information and indicative of an intent of a driver of one of the other vehicles. Further, predictions may be generated based on the model, which may be based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), a Dynamic Bayesian Network (DBN), or a combination thereof.

Claims

1. A system for turn predictions, comprising:

a navigation component receiving environment layout information of an operating environment through which a first vehicle is travelling and a current location of the first vehicle, wherein the environment layout information is indicative of a shape of a road segment;

a communication component receiving additional environment layout information directly from one or more of the other vehicles, wherein the additional environment layout information is indicative of the shape of the road segment, and wherein the communication component verifies the additional environment layout information against the environment layout information;

a modeling component building a model including the operating environment, the first vehicle, and one or more of the other vehicles based on the environment layout information and the additional environment layout information, the model indicative of an intent of a driver of one of the one or more other vehicles; and

a prediction component generating one or more predictions based on the model,

wherein the navigation component, the communication component, and the modeling component are implemented via a processing unit or a memory.

2. The system of claim 1, wherein the environment layout information or the additional environment layout information includes lane level information associated with the operating environment.

3. The system of claim 1, wherein the modeling component builds the model based on a Hidden Markov Model (HMM) where distributions of observations of the HMM are associated with a Gaussian Mixture Model (GMM).

4. The system of claim 1, wherein:

the communication component receives vehicle information from one or more of the other vehicles; and

the modeling component builds the model based on the vehicle information of one or more of the other vehicles.

5. The system of claim 1, comprising a sensor component receiving environmental layout information, wherein the modeling component builds the model based on the environment layout information.

6. The system of claim 1, wherein the prediction component generates a time-to-collision (TTC) prediction, a collision prediction, or a turn prediction.

7. The system of claim 1, comprising a mapping component providing information related to a number of lanes of a roadway, geometry of lanes at an intersection, or legal directions of motion from a lane based on one or more of the predictions and one or more traffic rules.

8. The system of claim 1, comprising an assist component determining one or more assist actions based on one or more of the predictions.

9. The system of claim 1, wherein the environment layout information or additional environment layout information includes number of lanes.

10. The system of claim 1, wherein the navigation component determines an intended travel path for the first vehicle based on a destination location and one or more of the predictions for one or more of the other vehicles indicative of motion of respective other vehicles.

11. A method for turn predictions, comprising:

receiving environment layout information of an operating environment through which a first vehicle is travelling and a current location of the first vehicle, wherein the environment layout information is indicative of a shape of a road segment;

detecting one or more other vehicles;

receiving additional environment layout information directly from one or more of the other vehicles, wherein the additional environment layout information is indicative of the shape of the road segment, and

verifying the additional environment layout information against the environment layout information;

building a model including the operating environment, the first vehicle, and one or more of the other vehicles based on the environment layout information and the additional environment layout information, the model indicative of an intent of a driver of one of the one or more other vehicles;

generating one or more predictions based on the model; and

rendering one or more of the predictions.

12. The method of claim 11, wherein the environment layout information or the additional environment layout information includes lane level information associated with the operating environment.

13. The method of claim 11, wherein building the model is based on a Hidden Markov Model (HMM) where distributions of observations of the HMM are associated with a Gaussian Mixture Model (GMM).

14. The method of claim 11, comprising:

receiving vehicle information from one or more of the other vehicles; and

building the model based on the vehicle information of one or more of the other vehicles.

15. The method of claim 11, comprising:

receiving environmental layout information from a sensor; and

building the model based on the environment layout information from the sensor.

16. The method of claim 11, comprising generating a time-to-collision (TTC) prediction, a collision prediction, or a turn prediction.

17. A system for turn predictions, comprising:

a navigation component receiving environment layout information of an operating environment through which a first vehicle is travelling and a current location of the first vehicle, wherein the environment layout information includes lane level information associated with the operating environment, a shape of a lane of a roadway within the operating environment, or a number of lanes of a roadway within the operating environment;

a sensor component detecting one or more other vehicles;

a communication component receiving additional environment layout information directly from one or more of the other vehicles, wherein the additional environment layout information is indicative of the lane of the roadway within the operating environment and wherein the communication component verifies the additional environment layout information against the environment layout information;

a modeling component building a model including the operating environment, the first vehicle, and one or more of the other vehicles based on the environment layout information and the additional environment layout information, the model indicative of an intent of a driver of one of the one or more other vehicles; and

a prediction component generating one or more predictions based on the model,

wherein the navigation component, the communication component, and the modeling component are implemented via a processing unit or a memory.

18. The system of claim 17, wherein the modeling component builds the model based on a Hidden Markov Model (HMM) where distributions of observations of the HMM are associated with a Gaussian Mixture Model (GMM).

19. The system of claim 17, wherein the prediction component generates a time-to-collision (TTC) prediction, a collision prediction, or a turn prediction.

20. The system of claim 17, wherein the navigation component determines an intended travel path for the first vehicle based on a destination location and one or more of the predictions for one or more of the other vehicles indicative of motion of respective other vehicles.