Monitoring vehicular operation risk using sensing devices

Patent metadata

Field	Value
Publication number	US-2019102689-A1
Application number	US-201715723671-A
Country	US
Kind code	A1
Filing date	Oct 3, 2017
Priority date	Oct 3, 2017
Publication date	Apr 4, 2019
Grant date	—

Abstract

Embodiments for monitoring risk associated with operating a vehicle by a processor. One or more behavior parameters of an operator of a vehicle may be learned in relation to the vehicle, one or more alternative vehicles, or a combination thereof using one or more sensing devices for a journey. A risk associated with the one or more learned behavior parameters for the journey may be assessed.

First claim

1 . A method, by a processor, for monitoring risk associated with operating a vehicle, comprising:
learning one or more learned behavior parameters of an operator of a vehicle in relation to the vehicle, one or more alternative vehicles, or a combination thereof using one or more sensing devices for a journey; and assessing a risk associated with the one or more learned behavior parameters.

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:
learn one or more learned behavior parameters of an operator of a vehicle for a journey in relation to the vehicle, one or more alternative vehicles, or a combination thereof using one or more sensing devices; and
assess a risk associated with the one or more learned behavior parameters.

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 learns one or more learned behavior parameters of an operator of a vehicle in relation to a journey using one or more sensing devices; and an executable portion that assesses a risk associated with the one or more learned behavior parameters.

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.

Assignees

• IBM

Inventors

• LASSOUED Yassine
• Mevissen Martin
• Monteil Julien
• Russo Giovanni

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title