Patent Issued for Detecting and mitigating local individual driver anomalous behavior (USPTO 11610441): State Farm Mutual Automobile Insurance Company

2023 APR 07 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Bernico, Michael (Bloomington, IL, US), filed on June 22, 2017, was published online on March 21, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11610441 is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Drivers generally drive the same routes on a regular basis. They may drive back and forth between their homes and work. Additionally, most drivers drive in a particular manner every time that they are driving. These behaviors may be known to a driver and are useful heuristics to allow the driver to focus on driving and other tasks; however, it may be difficult for drivers, or other drivers sharing the road, to know when a driver is driving erratically. Similarly, it is also difficult for other drivers to know when a driver near them is having a medical emergency, or undergoing some other anomalous situation that may be a dangerous situation for drivers on the road. Detecting and mitigating this anomalous behavior has many challenges.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “The present disclosure generally relates to systems and methods identifying anomalous driving behavior for a vehicle based on past driving behavior. Embodiments of example systems and methods are summarized below. The methods and systems summarized below may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In one embodiment, a computer-implemented method for identifying in anomalous driving behavior for a vehicle based on past driving behavior is disclosed. The method includes receiving a set of time-series driving data for the vehicle, wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle; performing machine learning operations on the set of time-series driving data; identifying a set of anomalous conditions in the time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous vehicle behavior; comparing the set of anomalous conditions to a set of historical time-series driving data for the vehicle; and generating a vehicle feedback based on the time-series driving data and the comparison of the set of anomalous conditions to the set of historical time-series driving data.

“In another embodiment, a computer implemented method for identifying anomalous driving behavior for a vehicle based on machine learning operations is disclosed. The method includes receiving a set of time-series driving data, wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle; performing machine learning operations on the set the set of time-series driving data; identifying a set of anomalous conditions in the time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous vehicle behavior; and modifying the machine learning operations based on the set of time-series driving data and the identified set of anomalous conditions.

“In yet another embodiment, a system for identifying anomalous driving behavior for a vehicle based on machine learning operations is disclosed. The system includes a network interface configured to interface with a processor; a plurality of sensors affixed to the vehicle and configured to interface with the processor; a memory configured to store non-transitory computer executable instructions and configured to interface with the processor. The processor may be configured to interface with the memory, wherein the processor is configured to execute the non-transitory computer executable instructions to cause the processor to: receive a set of time-series driving data, wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle; perform machine learning operations on the set the set of time-series driving data; identify a set of anomalous conditions in the time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous vehicle behavior; and generate a command based on the set of time-series driving data and the identified set of anomalous conditions.”

The claims supplied by the inventors are:

“1. A computer implemented method for identifying anomalous driving behavior for a vehicle based on past driving behavior, the method comprising: receiving, at one or more processors, a set of time-series driving data for the vehicle, wherein the set of time-series driving data includes at least one of: vehicle coordinate data, vehicle movement data, vehicle acceleration data, and vehicle brake system data; converting, at the one or more processors, the set of time-series driving data for the vehicle into a set of frequency data for the vehicle; performing, at the one or more processors, machine learning operations on the set of frequency data in order to identify irregular frequencies of particular driving events; identifying, at the one or more processors, based on the irregular frequencies of the particular driving events, a set of anomalous conditions in the time-series driving data, wherein the set of anomalous conditions comprise data indicative of an anomalous vehicle behavior, wherein the data indicative of the anomalous vehicle behavior includes data indicative of a medical situation; comparing, at the one or more processors, the set of anomalous conditions to a set of historical time-series driving data for the vehicle; and generating, at the one or more processors, a vehicle feedback based on the time-series driving data and the comparison of the set of anomalous conditions to the set of historical time-series driving data, wherein the vehicle feedback includes a vehicle feedback message to be provided to a driver operating the vehicle; and providing, by a communication system that is contained within the vehicle, the vehicle feedback message to the driver of the vehicle.

“2. The computer implemented method of claim 1, wherein the set of time-series driving data is basic safety message (BSM) data transmitted periodically by the vehicle.

“3. The computer implemented method of claim 2, wherein the basic safety message (BSM) data comprise a message identifier, a conditions dataset, a safety data set, and a status dataset.

“4. The computer implemented method of claim 1, wherein performing machine learning operations further comprises: generating, at the one or more processors, an isolation forest using the time-series driving data.

“5. The computer implemented method of claim 1, wherein comparing the set of anomalous conditions to a set of historical time-series driving data for the vehicle further comprises: comparing, at the one or more processors, the set of anomalous conditions to a set of threshold values.

“6. A system for identifying anomalous driving behavior for a vehicle based on machine learning operations, the system comprising: a network interface configured to interface with a processor; a plurality of sensors affixed to the vehicle and configured to interface with the processor; a memory configured to store non-transitory computer executable instructions and configured to interface with the processor; and the processor configured to interface with the memory, wherein the processor is configured to execute the non-transitory computer executable instructions to cause the processor to: receive a set of time-series driving data, wherein the set of time-series driving data includes at least one of: vehicle coordinate data, vehicle movement data, vehicle acceleration data, and vehicle brake system data; convert the set of time-series driving data for the vehicle into a set of frequency data for the vehicle; perform machine learning operations on the set of frequency data in order to identify irregular frequencies of particular driving events; identify, based on the irregular frequencies of the particular driving events, a set of anomalous conditions in the time-series driving data, wherein the set of anomalous conditions comprise data indicative of an anomalous vehicle behavior, wherein the data indicative of the anomalous vehicle behavior includes data indicative of a medical situation; and generate a vehicle feedback based on the set of time-series driving data and the identified set of anomalous conditions, wherein the vehicle feedback includes a vehicle feedback message to be provided to a driver operating the vehicle; and provide, by a communication system that is contained within the vehicle, the vehicle feedback message to the driver of the vehicle.

“7. The system of claim 6, wherein perform machine learning operations further comprises: generate an isolation forest using the time-series driving data.

“8. The system of claim 6, wherein identify the set of anomalous conditions in the time-series driving data further comprises: identify unusual frequencies for the time-series driving data.

“9. The system of claim 6, wherein generate a vehicle feedback further comprises: compare the set of anomalous conditions to a set of threshold values.”

URL and more information on this patent, see: Bernico, Michael. Detecting and mitigating local individual driver anomalous behavior. U.S. Patent Number 11610441, filed June 22, 2017, and published online on March 21, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11610441)&db=USPAT&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)