Detecting and Mitigating Local Individual Driver Anomalous Behavior

Abstract

Systems and methods for identifying anomalous driving behavior for a vehicle based on past driving behavior are disclosed herein. The method may include 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. 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.

What is claimed: 
     
         1 . A computer implemented method, the method comprising:
 receiving, at one or more processors, a set of time-series driving data for a 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;   identifying, at the one or more processors, based on irregular frequencies of the particular driving events, a set of anomalous conditions in the time-series driving data indicative of a medical situation;   generating, at the one or more processors, a vehicle feedback message based on the set of anomalous conditions; 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 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. 
     
     
         5 . The computer implemented method of  claim 1 , further comprising:
 performing, at the one or more processors, machine learning operations on the set of frequency data in order to identify the irregular frequencies of particular driving events.   
     
     
         6 . The computer implemented method of  claim 1 , further comprising:
 comparing, at the one or more processors, the set of anomalous conditions to a set of historical time-series driving data for the vehicle.   
     
     
         7 . The computer implemented method of  claim 6 , wherein generating the vehicle feedback message is further based on the comparison of the set of anomalous conditions to the set of historical time-series driving data for the vehicle. 
     
     
         8 . A 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 for a 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; 
 convert the set of time-series driving data for the vehicle into a set of frequency data for the vehicle; 
 identify, based on irregular frequencies of the particular driving events, a set of anomalous conditions in the time-series driving data indicative of a medical situation; and 
 generate a vehicle feedback message based on the identified set of anomalous conditions; and 
 provide, by a communication system that is contained within the vehicle, the vehicle feedback message to the driver of the vehicle. 
   
     
     
         9 . The system of  claim 8 , wherein generating the vehicle feedback message further includes comparing the set of anomalous conditions to a set of threshold values. 
     
     
         10 . The system of  claim 8 , wherein the set of time-series driving data is basic safety message (BSM) data transmitted periodically by the vehicle. 
     
     
         11 . The system of  claim 10 , wherein the basic safety message (BSM) data comprise a message identifier, a conditions dataset, a safety data set, and a status dataset. 
     
     
         12 . The system of  claim 10 , wherein the instructions, when executed by the processor, further cause the processor to:
 perform machine learning operations on the set of frequency data in order to identify the irregular frequencies of particular driving events.   
     
     
         13 . The system of  claim 10 , wherein the instructions, when executed by the processor, further cause the processor to:
 compare the set of anomalous conditions to a set of historical time-series driving data for the vehicle.   
     
     
         14 . The system of  claim 10 , wherein generating the vehicle feedback message is further based on the comparison of the set of anomalous conditions to the set of historical time-series driving data for the vehicle. 
     
     
         15 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to:
 receive a set of time-series driving data for a 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;   convert the set of time-series driving data for the vehicle into a set of frequency data for the vehicle;   identify, based on irregular frequencies of the particular driving events, a set of anomalous conditions in the time-series driving data indicative of a medical situation; and   generate a vehicle feedback message based on the identified set of anomalous conditions; and   provide, by a communication system that is contained within the vehicle, the vehicle feedback message to the driver of the vehicle.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein generating the vehicle feedback message further includes comparing the set of anomalous conditions to a set of threshold values. 
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the set of time-series driving data is basic safety message (BSM) data transmitted periodically by the vehicle. 
     
     
         18 . The non-transitory computer-readable medium of  claim 17 , wherein the basic safety message (BSM) data comprise a message identifier, a conditions dataset, a safety data set, and a status dataset. 
     
     
         19 . The non-transitory computer-readable medium of  claim 17 , wherein the instructions, when executed by the processor, further cause the processor to:
 perform machine learning operations on the set of frequency data in order to identify the irregular frequencies of particular driving events.   
     
     
         20 . The non-transitory computer-readable medium of  claim 17 , wherein the instructions, when executed by the processor, further cause the processor to:
 compare the set of anomalous conditions to a set of historical time-series driving data for the vehicle, and wherein generating the vehicle feedback message is further based on the comparison of the set of anomalous conditions to the set of historical time-series driving data for the vehicle.