Systems and methods for generating vehicle safety scores and predicting vehicle collision probabilities

Patent metadata

Field	Value
Publication number	US-2024157934-A1
Application number	US-202318499707-A
Country	US
Kind code	A1
Filing date	Nov 1, 2023
Priority date	Nov 15, 2022
Publication date	May 16, 2024
Grant date	—

Abstract

Systems and methods for generating vehicle safety scores and vehicle collision probabilities are provided. The methods involve operating at least one processor to: retrieve vehicle data originating from a telematics device installed in a vehicle, the vehicle data including a plurality of safety exception events performed by the vehicle; determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types; determine plurality of collision sub-probabilities using a plurality of collision probability models and the plurality of exception rates, each collision probability model associated with one of the exception event types and operable to predict one of the collision sub-probabilities based on one of the exception rates of the associated exception event type; and determine a collision probability for the vehicle based on the plurality of collision sub-probabilities.

First claim

1 . A system for predicting vehicle collision probabilities, the system comprising:
at least one data store operable to store vehicle data originating from a telematics device installed in a vehicle, the vehicle data comprising a plurality of safety exception events performed by the vehicle, the plurality of safety exception events comprising a plurality of exception event types; at least one processor in communication with the at least one data store, the at least one processor operable to:
retrieve the vehicle data;
determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types;
determine plurality of collision sub-probabilities using a plurality of collision probability models and the plurality of exception rates, each collision probability model associated with one of the exception event types and operable to predict one of the collision sub-probabilities based on one of the exception rates of the associated exception event type; and
determine a collision probability for the vehicle based on the plurality of collision sub-probabilities, the collision probability representing a risk of collision for the vehicle.

2 . The system of claim 1 , wherein determining the collision probability of the vehicle comprises:
determining a vehicle class of the vehicle; and scaling the collision probability based on the vehicle class of the vehicle.

3 . The system of claim 1 , wherein determining the collision probability of the vehicle comprises:
applying a predetermined weight to each collision sub-probability.

4 . The system of claim 1 , wherein the at least one processor is operable to:
determine a vehicle collision probability benchmark for the vehicle based on the collision probability score for the vehicle and a plurality of collision probabilities for a plurality of comparable vehicles.

5 . The system of claim 4 , wherein the at least one processor is operable to:
identify the plurality of comparable vehicles from a plurality of vehicles by:
using a clustering algorithm to identify a plurality of first vehicle clusters based on an area of operation of each vehicle in the plurality of vehicles; and
using a comparison algorithm to identify a plurality of second vehicle clusters based on the centroid of each first vehicle cluster; and
identify the second vehicle cluster containing the vehicle as the plurality of comparable vehicles.

6 . The system of claim 1 , wherein the at least one processor is operable to:
determine a fleet collision probability for a fleet comprising the vehicle based on the vehicle collision probability for the vehicle and a vehicle collision probability for each other vehicle in the fleet.

7 . The system of claim 6 , wherein the at least one processor is operable to:
determine a fleet collision probability benchmark for the fleet based on the fleet collision probability for the fleet and a plurality of fleet collision probabilities for a plurality of comparable fleets.

8 . The system of claim 7 , wherein the at least one processor is operable to:
identify the plurality of comparable fleets from a plurality of fleets by using a clustering algorithm to identify a plurality of fleet clusters based on a vehicle type of each vehicle in each fleet in the plurality of fleets; and identify the fleet cluster containing the fleet as the plurality of comparable fleets.

9 . The system of claim 1 , wherein:
the plurality of exception event types comprises harsh events and speeding events; and the plurality of exception rates comprises harsh event rates and speeding event rates.

10 . A method for predicting vehicle collision probabilities, the method comprising operating at least one processor to:
retrieve vehicle data originating from a telematics device installed in a vehicle, the vehicle data comprising a plurality of safety exception events performed by the vehicle, the plurality of safety exception events comprising a plurality of exception event types; determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types; determine plurality of collision sub-probabilities using a plurality of collision probability models and the plurality of exception rates, each collision probability model associated with one of the exception event types and operable to predict one of the collision sub-probabilities based on one of the exception rates of the associated exception event type; and determine a collision probability for the vehicle based on the plurality of collision sub-probabilities, the collision probability representing a risk of collision for the vehicle.

11 . The method of claim 10 , wherein determining the collision probability of the vehicle comprises:
determining a vehicle class of the vehicle; and scaling the collision probability based on the vehicle class of the vehicle.

12 . The method of claim 10 , wherein determining the collision probability of the vehicle comprises:
applying a predetermined weight to each collision sub-probability.

13 . The method of claim 10 , further comprising operating the at least one processor to:
determine a vehicle collision probability benchmark for the vehicle based on the collision probability score for the vehicle and a plurality of collision probabilities for a plurality of comparable vehicles.

14 . The method of claim 13 , further comprising operating the at least one processor to:
identify the plurality of comparable vehicles from a plurality of vehicles by:
using a clustering algorithm to identify a plurality of first vehicle clusters based on an area of operation of each vehicle in the plurality of vehicles; and
using a comparison algorithm to identify a plurality of second vehicle clusters based on the centroid of each first vehicle cluster; and
identify the second vehicle cluster containing the vehicle as the plurality of comparable vehicles.

15 . The method of claim 10 , further comprising operating the at least one processor to:
determine a fleet collision probability for a fleet comprising the vehicle based on the vehicle collision probability for the vehicle and a vehicle collision probability for each other vehicle in the fleet.

16 . The method of claim 15 , further comprising operating the at least one processor to:
determine a fleet collision probability benchmark for the fleet based on the fleet collision probability for the fleet and a plurality of fleet collision probabilities for a plurality of comparable fleets.

17 . The method of claim 16 , further comprising operating the at least one processor to:
identify the plurality of comparable fleets from a plurality of fleets by using a clustering algorithm to identify a plurality of fleet clusters based on a vehicle type of each vehicle in each fleet in the plurality of fleets; and identify the fleet cluster containing the fleet as the plurality of comparable fleets.

18 . The method of claim 10 , wherein:
the plurality of exception event types comprises harsh events and speeding events; and the plurality of exception rates comprises harsh event rates and speeding event rates.

19 . A non-transitory computer readable medium having instructions stored thereon executable by at least one processor to implement a method for predicting vehicle collision probabilities, the method comprising operating the at least one processor to:
retrieve veh

Assignees

• Geotab Inc

Inventors

• Zhang Xin
• Hines Gregory Gordon Douglas
• Yu Jiawei
• Petersen Willem
• Tiwari Tuhin
• Murugesan Meenakshi Sundaram
• Siddique Javed
• Yan Jason Jiajie
• Durgam Narasimha Rao
• Zhang Li

Classifications

• G08G1/20

  Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles · CPC title

• G08G1/017

  identifying vehicles (G08G1/015, G08G1/054 take precedence) · CPC title

• G08G1/0141

  for traffic information dissemination · CPC title

• G08G1/0133

  for classifying traffic situation · CPC title

• G08G1/0129

  for creating historical data or processing based on historical data · CPC title