Vehicle systems for identifying a driver

• US 10,787,075 B2
• Filed: 08/29/2017
• Issued: 09/29/2020
• 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.
• Primary Examiner(s): Lee, Tyler J
• Application Number:
  US15/689,451
• Publication Number:
  US 20180056786A1
• Time in Patent Office: 1,127 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

An apparatus comprising a sensor interface, a memory and a processor. The sensor interface may be configured to receive sensor data samples during operation of a vehicle. At least one of the sensor data samples may be driver-related. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to (i) analyze the sensor data samples stored in the memory to detect a pattern and (ii) generate a likelihood that a particular driver is operating the vehicle based on the pattern detected.

Claims

1. An apparatus comprising:

a sensor interface configured to receive sensor data samples during operation of a vehicle, wherein at least one of said sensor data samples is driver-related;

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 historical pattern of said vehicle with respect to at least one of a route and a destination, (ii) predict when said vehicle is repeating said historical pattern using at least one of statistical route analysis and statistical destination analysis, and (iii) determine, based at least in part on the predicting, a probability that a particular driver identity corresponds to a driver operating said vehicle.

2. The apparatus according to claim 1, wherein said processor is further configured to statistically determine a most likely driver identity corresponding to the particular driver operating said vehicle based upon multiple patterns.

3. The apparatus according to claim 1, wherein said driver-related sensor data comprises one or more of (a) command inputs, (b) historical driving patterns, and (c) physical attributes of said driver.

4. The apparatus according to claim 3, wherein said historical driving patterns comprise a destination and a time of day.

5. The apparatus according to claim 1, wherein (i) said processor further comprises a classification module and (ii) said sensor data samples are implemented as arrays of data.

6. The apparatus according to claim 5, wherein said classification module is configured to classify said arrays of data into clusters associated with one or more drivers.

7. The apparatus according to claim 6, wherein said clusters are used to infer a driver identity of one driver of said vehicle from a driver identity of another driver of said vehicle based on driver behaviors.

8. The apparatus according to claim 1, wherein said processor is further configured to determine said probability based at least in part on a time of day.

9. The apparatus of claim 1, wherein the processor is configured to infer a destination based on the particular driver identity, a day of the week, and a time of day.

10. The apparatus of claim 1, wherein the processor is configured to estimate an energy usage of the vehicle along a route segment based on the particular driver identity.

11. The apparatus of claim 1, wherein the processor is configured to automatically adjust a speed of the vehicle along a route segment to match a lowest energy speed associated with the route segment and associated with the particular driver identity.

12. The apparatus of claim 1, wherein the processor is configured to determine a probable optimal speed for a route segment based at least in part on historical information on energy efficiency of multiple prior trips along the route segment driven by the driver corresponding with the particular driver identity, wherein the probable optimal speed is configured to increase energy efficiency of the vehicle.

13. The apparatus of claim 1, wherein the processor is configured to determine, for a single route segment, multiple probable optimal speeds, each probable optimal speed corresponding with a different driver.

14. The apparatus of claim 1, wherein the processor is configured to initiate a notification to the driver of a fuel efficient speed, wherein the fuel efficient speed is based on the particular driver identity.

15. The apparatus of claim 1, wherein the processor is configured to, based at least in part on a start location and the particular driver identity:

determine a plurality of intermediate destinations and end destinations and, for each intermediate destination and each end destination, determine a probability of the vehicle arriving thereat.

16. A method for determining an identity of a driver, comprising the steps of:

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

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

(C) analyzing, by one or more processors, said sensor data to detect a historical pattern of said vehicle with respect to at least one of a route and a destination;

(D) predicting, by said one or more processors, when said vehicle is repeating said historical pattern using at least one of statistical route analysis and statistical destination analysis; and

(E) generating, by said one or more processors based at least in part on the predicting, a probability that a particular driver identity corresponds to a driver operating said vehicle.

17. The method of claim 16, further comprising determining a probable optimal speed for a route segment based at least in part on historical information of energy efficiency of multiple prior trips along the route segment driven by the driver corresponding with the particular driver identity, wherein the probable optimal speed is configured to increase energy efficiency of the vehicle.

18. The method according to claim 16, further comprising deriving one or more command inputs based on the particular driver identity.

19. A vehicle comprising:

a power train comprising a plurality of wheels;

a sensor interface configured to receive sensor data samples during operation of a vehicle, wherein at least one of said sensor data samples is driver-related;

at least one 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 at least one storage device to detect a historical pattern of said vehicle with respect to at least one of a route and a destination, (ii) predict when said vehicle is repeating said historical pattern using at least one of statistical route analysis and statistical destination analysis, and (iii) determine, based at least in part on the predicting, a probability that a particular driver identity corresponds to a driver operating said vehicle.

20. The vehicle according to claim 19, further comprising a device configured to (i) receive said sensor data samples from said sensors and (ii) derive at least one of command inputs, historical driving patterns, or physical attributes of said driver.