Method and system for dynamically adapting user interfaces in vehicle navigation systems to minimize interaction complexity

• US 9,170,119 B2
• Filed: 09/24/2013
• Issued: 10/27/2015
• Est. Priority Date: 09/24/2013
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Nikovski, Daniel Nikolaev, Hershey, John R, Harsham, Bret, Le Roux, Jonathan
• Primary Examiner(s): Black, Thomas G
• Assistant Examiner(s): HUYNH, LUAT T
• Application Number:
  US14/035,502
• Publication Number:
  US 20150088422A1
• Time in Patent Office: 763 Days
• Field of Search: 701/538, 701/301, 701/117, 701/532, 701/24
• US Class Current: 1/1
• CPC Class Codes: G01C 21/36 Input/output arrangements for on-board computers
• 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: G06F 18/2415 based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate

Abstract

A method adapts a user interface of a vehicle navigation system. Based on an input vector representing a current state related to the vehicle, probabilities of actions are predicted to achieve a next state using a predictive model representing previous states. Then, a subset of the actions with highest probabilities that minimize a complexity of interacting with the vehicle navigation system are displayed in the vehicle.

Claims

1. A method for adapting a user interface of a vehicle navigation system in a vehicle, comprising the steps:

predicting, based on an input vector representing a current state related to the vehicle, probabilities of actions to achieve a next state using a predictive model representing previous states, wherein the input vector is x=[x₁, x₂, . . . , X_N]^T of N dimensions, wherein T is a transpose operator; and

displaying, in the vehicle, on the user interface to adapt the user interface, a subset of the actions with highest probabilities that minimize a complexity of interacting with the vehicle navigation system using the user interface as adapted, and wherein the actions are a vector y=[y₁, y₂, . . . , y_P] ^T of P Boolean variables.

2. The method of claim 1, wherein the displaying is on a main display device of the vehicle navigation system.

3. The method of claim 1, wherein the input vector is acquired periodically, and the displaying is updated periodically.

4. The method of claim 1, wherein the current state includes information related to external states and internal states of the vehicle.

5. The method of claim 4, wherein the external states include a location, time, traffic, and external environment information, and the internal state includes vehicle operational measurements, internal environment, and operator information.

6. The method of claim 5, wherein the external information is acquired via wireless communication with external databases.

7. The method of claim 4, further comprising:

synchronizing the information; and

extracting the input vector after the synchronizing.

8. The method of claim 1, wherein the predictive model relates the current state to a next to be performed using the previous states.

9. The method of claim 1, further comprising:

organizing and displaying the actions hierarchically as a tree.

10. The method of claim 1, wherein some actions are directly related to the input vector using derived input features.

11. The method of claim 1, wherein the probabilities of the actions are based on a time difference between the current state and the previous states.

12. The method of claim 1, wherein the prediction is based on highly predictive sub-sequences of previous states series.

13. The method of claim 1, wherein the predicting considers mutually exclusive actions.

14. The method of claim 1, further comprising:

learning the previous states during a training phase using training vectors.

15. The method of claim 14, wherein the learning uses logistic regression, decision trees, naï

ve Bayes networks, or support vector machines.

16. The method of claim 14, wherein the learning is performed periodically.