VEHICLE DESTINATION PREDICTION

• US 20180065484A1
• Filed: 09/08/2017
• Published: 03/08/2018
• Est. Priority Date: 11/17/2005
• Status: Active Grant

• Current Assignee: iQar Inc.
• Original Assignee: Invently Automotive Inc. (iQar Inc.)
• Inventors: Koebler, Martin, Goldstein, Nicole G., Brown, Stephen J., Harper, Jason H.
• Granted Patent: US 10,829,002 B2
• Time in Patent Office: Days
• CPC Class Codes: B60K 2360/174 Economic driving
• CPC Class Codes: B60K 35/00 Instruments specially adapted for vehicles; Arrangement of instruments in or on vehicles
• CPC Class Codes: B60K 35/20 Output arrangements, i.e. from vehicle to user, associated with vehicle functions or specially adapted therefor
• CPC Class Codes: B60K 35/28 characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver
• CPC Class Codes: B60L 15/2045 for optimising the use of energy
• CPC Class Codes: B60L 2220/44 Wheel Hub motors, i.e. integrated in the wheel hub
• CPC Class Codes: B60L 2240/12 Speed
• CPC Class Codes: B60L 2240/421 Speed
• CPC Class Codes: B60L 2240/423 Torque
• CPC Class Codes: B60L 2240/441 Speed
• CPC Class Codes: B60L 2240/443 Torque
• CPC Class Codes: B60L 2240/445 Temperature
• CPC Class Codes: B60L 2240/545 Temperature
• CPC Class Codes: B60L 2240/547 Voltage
• CPC Class Codes: B60L 2240/549 Current
• CPC Class Codes: B60L 2240/622 by satellite navigation
• CPC Class Codes: B60L 2240/642 Slope of road
• CPC Class Codes: B60L 2240/645 Type of road
• CPC Class Codes: B60L 2240/647 Surface situation of road, e.g. type of paving
• CPC Class Codes: B60L 2240/662 Temperature
• CPC Class Codes: B60L 2240/665 : Light intensity
• CPC Class Codes: B60L 2240/667 : Precipitation
• CPC Class Codes: B60L 2240/68 : Traffic data
• CPC Class Codes: B60L 2250/10 : by alarm
• CPC Class Codes: B60L 2250/16 : by display
• CPC Class Codes: B60L 2260/28 : Four wheel or all wheel drive
• CPC Class Codes: B60L 2260/50 : by future state prediction
• CPC Class Codes: B60L 3/12 : Recording operating variables ; Monitoring of operating variables
• CPC Class Codes: B60L 50/62 : charged by low-power generators primarily intended to support the batteries, e.g. range extenders
• CPC Class Codes: B60L 58/12 : responding to state of charge [SoC]
• CPC Class Codes: B60L 58/16 : responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
• CPC Class Codes: B60L 7/12 : for vehicles propelled by dc motors
• CPC Class Codes: B60L 8/003 : Converting light into electric energy, e.g. by using photo-voltaic systems
• CPC Class Codes: B60T 1/10 : by utilising wheel movement for accumulating energy, e.g. driving air compressors
• CPC Class Codes: B60T 13/586 : the retarders being of the electric type
• CPC Class Codes: B60T 2201/04 : Hill descent control
• CPC Class Codes: B60T 2210/36 : Global Positioning System [GPS]
• CPC Class Codes: B60T 7/18 : operated by wayside apparatus
• CPC Class Codes: B60T 7/22 : initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle , or by means of contactless obstacle detectors mounted on the vehicle
• CPC Class Codes: B60W 10/06 : including control of combustion engines
• CPC Class Codes: B60W 10/11 : Stepped gearings
• CPC Class Codes: B60W 20/12 : using control strategies taking into account route information estimation or calculation of non-directly measurable driving parameters B60W40/00
• CPC Class Codes: B60W 2552/20 : Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
• CPC Class Codes: B60W 2556/10 : Historical data
• CPC Class Codes: B60W 2556/50 : of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2720/103 : Speed profile
• CPC Class Codes: B60W 30/143 : Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 50/0097 : Predicting future conditions
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• 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 7/00 : Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/085 : using electronic data carriers
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096844 : where the complete route is dynamically recomputed based on new data
• CPC Class Codes: Y02T 10/40 : Engine management systems
• CPC Class Codes: Y02T 10/62 : Hybrid vehicles
• CPC Class Codes: Y02T 10/64 : Electric machine technologies in electromobility
• CPC Class Codes: Y02T 10/70 : Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 : Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 : Electric energy management in electromobility
• CPC Class Codes: Y02T 90/16 : Information or communication technologies improving the operation of electric vehicles

Abstract

Described herein are devices, systems, and methods for managing the power consumption of an automotive vehicle, and thereby for optimizing the power consumption of the vehicle. The devices and systems for managing the power consumption of the vehicle typically include power management logic that can calculate an applied power for the vehicle engine based on information provided from the external environment of the vehicle, the operational status of the vehicle, one or more command inputs from a driver, and one or more operational parameters of the vehicle.

Claims

1. An apparatus comprising:

a sensor interface configured to receive sensor data samples during operation of a vehicle;

a memory configured to store said sensor data samples over a number of points in time; and

a processor configured to (i) analyze said sensor data samples stored in said memory to detect a pattern and (ii) predict a destination of a driver in response to (a) said pattern and (b) historical data associated with operation of said vehicle, wherein (i) at least one of said sensor data samples is driver-related and (ii) at least one of said sensor data samples comprises telemetry data.

2. The apparatus according to claim 1, wherein said telemetry data comprises map coordinates determined in response to a GPS signal.

3. The apparatus according to claim 1, wherein said telemetry data comprises map coordinates determined by a map service.

4. The apparatus according to claim 1, wherein said destination of said driver is predicted based on statistical destination logic.

5. The apparatus according to claim 1, wherein said processor is further configured to generate a likelihood that a particular driver is operating said vehicle based on said pattern detected.

6. The apparatus according to claim 1, wherein said telemetry data is used to determine route segments.

7. The apparatus according to claim 6, wherein said pattern comprises which of said route segments said vehicle has traveled.

8. The apparatus according to claim 1, wherein said apparatus is further configured to transmit said predicted destination to a remote database.

9. The apparatus according to claim 8, wherein said remote database is configured to receive a plurality of said predicted destinations and generate guidance information based on said plurality of said predicted destinations.

10. The apparatus according to claim 8, wherein said remote database is configured to (i) receive said sensor data samples and store said sensor data samples as said historical data and (ii) present said historical data to said apparatus.

11. The apparatus according to claim 1, wherein said memory is further configured to store said historical data.

12. The apparatus according to claim 1, wherein said historical data comprises route segments traveled, a sequence of route segments traveled and travel destinations.

13. The apparatus according to claim 1, wherein said historical data comprises information about operation of said vehicle.

14. The apparatus according to claim 1, wherein at least one of said sensor data samples comprises a data sample from an optical sensor.

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

(A) monitoring a plurality of sensors to generate sensor data samples during operation of a vehicle;

(B) storing said sensor data samples over a number of points in time;

(C) analyzing said sensor data samples to detect a pattern; and

(D) predicting a destination of a driver in response to (a) said pattern and (b) historical data associated with operation of said vehicle, wherein (i) at least one of said sensor data samples is driver-related and (ii) at least one of said sensor data samples comprises telemetry data.

16. A vehicle comprising:

a power train;

a drive train;

a sensor interface configured to receive sensor data samples during operation of a vehicle;

a storage device configured to store said sensor data samples over a number of points in time; and

a processing device configured to (i) analyze said sensor data samples stored in said storage device to detect a pattern and (ii) predict a destination of a driver in response to (a) said pattern and (b) historical data associated with operation of said vehicle, wherein (i) at least one of said sensor data samples is driver-related and (ii) at least one of said sensor data samples comprises telemetry data.

17. The vehicle according to claim 16, further comprising:

an apparatus configured to (i) receive said predicted destination and (ii) adapt an operation of said vehicle based on said predicted destination.