SITUATION-AWARE ROUTE AND DESTINATION PREDICTIONS

• US 20150354978A1
• Filed: 06/09/2014
• Published: 12/10/2015
• Est. Priority Date: 06/09/2014
• Status: Active Grant

• Current Assignee: Audi AG (Volkswagen AG), Volkswagen AG
• Original Assignee: Audi AG (Volkswagen AG), Volkswagen AG
• Inventors: GERLACH, Simon
• Granted Patent: US 9,500,493 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 21/362 received from an external device or application, e.g. PDA, mobile phone or calendar application
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• 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 1/096894 where input is assisted by the navigation device, i.e. the user does not type the complete name of the destination, e.g. using zip codes, telephone numbers, progressively selecting from initial letters

Abstract

A method for predicting a destination of a vehicle includes receiving vehicle data from a plurality of sensors, the vehicle data including at least a current location and a current time, determining a plurality of usage scenarios based on the vehicle data, accessing historical vehicle data and user data from a database, assigning, based on the vehicle data, a likelihood value to each of the plurality of usage scenarios, and predicting a set of destinations and routes for each of the plurality of usage scenarios.

Claims

1. A method for predicting a destination of a vehicle, the method comprising:

receiving vehicle data from a plurality of sensors, the vehicle data including at least a current location and a current time;

determining a plurality of usage scenarios based on the vehicle data;

accessing historical vehicle data and user data from a database;

assigning, based on the vehicle data, a likelihood value to the plurality of usage scenarios; and

predicting a set of destinations and routes for the plurality of usage scenarios.

2. The method according to claim 2, further comprising assigning a weight to the usage scenarios and set of destinations and routes based on the likelihood value and the historical data.

3. The method according to claim 2, further comprising sorting the usage scenarios and set of destinations and routes based on the weight to obtain a mostly likely set for predictions.

4. The method according to claim 3, presenting the most likely set of predictions to a user.

5. The method according to claim 4, further comprising presenting the most likely set of predictions to a user.

6. A system, comprising:

a sensor configured to detect vehicle data;

a situation detection unit configured to receive the vehicle data from the sensor and to determine a plurality of common usage scenarios based on the vehicle data, the situation detection unit configured to determine and assign a likelihood value for the plurality of common usage scenarios; and

a navigation system control unit configured to receive the vehicle data from the sensor and the plurality of common usage scenarios and the likelihood value from the situation detection unit,

wherein the navigation system control unit is configured to determine a set of destinations for the common usage scenarios.

7. The system according to claim 6, further comprising a historical database containing vehicle data for prior vehicle trips.

8. The system according to claim 6, wherein the sensor comprises a plurality of sensors configured to detect a variety of vehicle data.