Monitoring vehicular operation risk using sensing devices

1. A method, by a processor, for monitoring risk associated with operating a vehicle, comprising:

receiving, from one or more sensing devices, data corresponding to an operation of a vehicle in relation to one or more alternative vehicles in proximity to the vehicle;

training a machine learning model to identify one or more learned behavior patterns of an operator of the vehicle using the data and additional data from external data sources;

learning, by the trained machine learning model, the one or more learned behavior parameters of the operator of the vehicle in relation to the vehicle and the one or more alternative vehicles using the one or more sensing devices for a journey; and

assessing a risk associated with the one or more learned behavior parameters according to the trained machine learning model.

2. The method of claim 1 , further including determining an acceleration, speed, position, or a combination thereof of the vehicle using the one or more sensing devices associated with the vehicle.

3. The method of claim 2 , further including determining and tracking speed, acceleration, or a combination thereof of the one or more alternative vehicles and a position of the one or more alternative vehicles in relation to the vehicle using the one or more sensing devices associated with the vehicle, wherein the one or more alternative vehicles are in front of the vehicle, behind the vehicle, adjacent to the vehicle, or a combination thereof.

4. The method of claim 1 , further including monitoring driving behavior of the operator using the one or more learned behavior parameters.

5. The method of claim 1 , wherein learning the one or more learned behavior parameters further includes learning one or more contextual factors relating to the journey using the one or more sensing devices associated with the vehicle, wherein the one or more contextual factors include traffic data, weather data, road conditions, road types, or a combination thereof.

6. The method of claim 1 , further including detecting an anomaly in driving behavior of the operator based on a real-time comparison operation between the one or more learned behavior parameters and a previously learned behavior parameter of the operator, a plurality of vehicle operators, or a combination thereof.

7. The method of claim 1 , further including providing one or more mitigating actions or alerts to reduce the risk, wherein the one or more sensing devices include one or more positioning sensors, one or more Internet of Things (IoT) devices, or a combination thereof.

8. A system for monitoring risk associated with operating a vehicle, comprising:

one or more computers with executable instructions that when executed cause the system to:

receive, from one or more sensing devices, data corresponding to an operation of a vehicle in relation to one or more alternative vehicles in proximity to the vehicle;

train a machine learning model to identify one or more learned behavior patterns of an operator of the vehicle using the data and additional data from external data sources;

learn, by the trained machine learning model, the one or more learned behavior parameters of the operator of the vehicle in relation to the vehicle and the one or more alternative vehicles using the one or more sensing devices for a journey; and

assess a risk associated with the one or more learned behavior parameters according to the trained machine learning model.

9. The system of claim 8 , wherein the executable instructions further determine and track an acceleration, speed, position, or a combination thereof of the vehicle using the one or more sensing devices associated with the vehicle.

10. The system of claim 9 , wherein the executable instructions further determine and track speed, acceleration, or a combination thereof of the one or more alternative vehicles and a position of the one or more alternative vehicles in relation to the vehicle using the one or more sensing devices associated with the vehicle, wherein the one or more alternative vehicles are in front of the vehicle, behind the vehicle, adjacent to the vehicle, or a combination thereof.

11. The system of claim 8 , wherein the executable instructions further monitor driving behavior of the operator using the one or more learned behavior parameters.

12. The system of claim 8 , wherein learning the one or more learned behavior parameters further includes learning one or more contextual factors relating to the journey using the one or more sensing devices associated with the vehicle, wherein the one or more contextual factors include traffic data, weather data, road conditions, road types, or a combination thereof.

13. The system of claim 8 , wherein the executable instructions further detect an anomaly in driving behavior of the operator based on a real-time comparison operation between the one or more learned behavior parameters and a previously learned behavior parameter of the operator, one or more alternative drivers of the one or more alternative vehicles, or a combination thereof.

14. The system of claim 8 , wherein the executable instructions further provide one or more mitigating actions or alerts to reduce the risk, wherein the one or more sensing devices include one or more positioning sensors, one or more Internet of Things (IoT) devices, or a combination thereof.

15. A computer program product for monitoring risk associated with operating a vehicle by a processor, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

an executable portion that receives, from one or more sensing devices, data corresponding to an operation of a vehicle in relation to one or more alternative vehicles in proximity to the vehicle;

an executable portion that trains a machine learning model to identify one or more learned behavior patterns of an operator of the vehicle using the data and additional data from external data sources;

an executable portion that learns, by the trained machine learning model, the one or more learned behavior parameters of the operator of the vehicle in relation to the vehicle and the one or more alternative vehicles using the one or more sensing devices for a journey; and

an executable portion that assesses a risk associated with the one or more learned behavior parameters according to the trained machine learning model.

16. The computer program product of claim 15 , further including an executable portion that:

determines and tracks an acceleration, speed, position, or a combination thereof of the vehicle using the one or more sensing devices associated with the vehicle; and

determines and tracks speed, acceleration, or a combination thereof of the one or more alternative vehicles and a position of the one or more alternative vehicles in relation to the vehicle using the one or more sensing devices associated with the vehicle, wherein the one or more alternative vehicles are in front of the vehicle, behind the vehicle, adjacent to the vehicle, or a combination thereof.

17. The computer program product of claim 15 , further including an executable portion that monitors driving behavior of the operator using the one or more learned behavior parameters.

18. The computer program product of claim 15 , wherein learning the one or more learned behavior parameters further includes learning one or more contextual factors relating to the journey using the one or more sensing devices associated with the vehicle, wherein the one or more contextual factors include traffic data, weather data, road conditions, road types, or a combination thereof.

19. The computer program product of claim 15 , further including an executable portion that detects an anomaly in driving behavior of the operator based on a real-time comparison operation between the one or more learned behavior parameters and a previously learned behavior parameter of the operator, a plurality of vehicle operators, or a combination thereof.

20. The computer program product of claim 15 , further including an executable portion that provides one or more mitigating actions or alerts to reduce the risk, wherein the one or more sensing devices include one or more positioning sensors, one or more Internet of Things (IoT) devices, or a combination thereof.