Vehicle systems for identifying a driver
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.
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.