EP-4742211-A1 - DETERMINING PERSONALIZED DRIVER RISK

EP4742211A1EP 4742211 A1EP4742211 A1EP 4742211A1EP-4742211-A1

Abstract

A computer-implemented method for determining personalized driving risk includes collecting, as collected data, location data and driving data associated with a one or more trips associated with a driver. Based on the collected data, one or more risky behaviors of the driver is identified. The collected data is combined with mapping data associated with the location data. Based on the combined data, driver behavior data associated with the driver is determined. Behavioral information associated with the driver and driving suggestion information is provided on a computer display of a mobile computing device.

Inventors

STEELE, Patrick Robert

Assignees

Cambridge Mobile Telematics Inc.

Dates

Publication Date: 20260513
Application Date: 20251107

Claims (15)

A computer-implemented method for determining personalized driver risk, comprising: collecting, as collected data, location data and driving data associated with a one or more trips associated with a driver; identifying, based on the collected data, one or more risky behaviors of the driver; combining, as combined data, the collected data with mapping data associated with the location data; determining, based on the combined data, driver behavior data associated with the driver; and providing behavioral information associated with the driver and driving suggestion information on a computer display of a mobile computing device.
The computer-implemented method of claim 1, comprising: identifying, based on the combined data and for each risky behavior of the one or more risky behaviors, a traffic infraction associated with each risky behavior of the one or more risky behaviors.
The computer-implemented method of claim 2, comprising: identifying, based on the combined data and for each risky behavior of the one or more risky behaviors, a type of road associated with each risky behavior of the one or more risky behaviors.
The computer-implemented method of claim 1, wherein the collected data includes measurements from vehicle-based sensors, a mobile computing device, or Internet-of- Things (IoT) sensors.
The computer-implemented method of claim 1, wherein the one or more risky behaviors include a mobile-computing-device-based distraction, sudden braking, sudden acceleration, sudden deacceleration, or traveling at a high speed.
The computer-implemented method of claim 1, wherein traffic infractions include failing to stop at a stop sign, failing to observe traffic signals, failing to observe traffic signs, speeding through reduced speed zones, performing illegal U-turns, improper parking or stopping, or driving a wrong direction on a road.
The computer-implemented method of claim 1, comprising: analyzing, based on the location data and to generate other driver behavior data, driver behavior associated with one or more other drivers.
The computer-implemented method of claim 7, comprising: generating, based on the other driver behavior data and the driver behavior data, an individual risk rating for the driver; or generating, based on the other driver behavior data and the driver behavior data, the behavioral information associated with the driver and driving suggestion information.
The computer-implemented method of claim 7, wherein the driver behavior data associated with the driver and the other driver behavior data is based on road geometry and specific roads traveled.
The computer-implemented method of claim 1, comprising: training machine-learning (ML) models to predict, based on the collected data, whether a particular behavior of the driver is risky and to identify patterns of risky behavior for the driver, wherein the ML models are training using a dataset containing data indicating risky driving behavior.
A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations, comprising: collecting, as collected data, location data and driving data associated with a one or more trips associated with a driver; identifying, based on the collected data, one or more risky behaviors of the driver; combining, as combined data, the collected data with mapping data associated with the location data; determining, based on the combined data, driver behavior data associated with the driver; and providing behavioral information associated with the driver and driving suggestion information on a computer display of a mobile computing device.
The non-transitory, computer-readable medium of claim 11, further comprising at least one of: identifying, based on the combined data and for each risky behavior of the one or more risky behaviors, a type of road associated with each risky behavior of the one or more risky behaviors; or identifying, based on the combined data and for each risky behavior of the one or more risky behaviors, a traffic infraction associated with each risky behavior of the one or more risky behaviors; wherein traffic infractions include failing to stop at a stop sign, failing to observe traffic signals, failing to observe traffic signs, speeding through reduced speed zones, performing illegal U-turns, improper parking or stopping, or driving a wrong direction on a road.
The non-transitory, computer-readable medium of claim 11, comprising: analyzing, based on the location data and to generate other driver behavior data, driver behavior associated with one or more other drivers; and generating, based on the other driver behavior data and the driver behavior data, an individual risk rating for the driver; or generating, based on the other driver behavior data and the driver behavior data, the behavioral information associated with the driver and driving suggestion information.
The non-transitory, computer-readable medium of claim 11, comprising: training machine-learning (ML) models to predict, based on the collected data, whether a particular behavior of the driver is risky and to identify patterns of risky behavior for the driver, wherein the ML models are training using a dataset containing data indicating risky driving behavior.
A computer-implemented system, comprising: one or more computers; and one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations, comprising: collecting, as collected data, location data and driving data associated with a one or more trips associated with a driver; identifying, based on the collected data, one or more risky behaviors of the driver; combining, as combined data, the collected data with mapping data associated with the location data; determining, based on the combined data, driver behavior data associated with the driver; and providing behavioral information associated with the driver and driving suggestion information on a computer display of a mobile computing device.

Description

BACKGROUND Traditional auto insurance is based on high-level statistical data models that differentiate an individual driver from a population of drivers, using data such as age, sex, years driving, and accident history. The introduction of telematics-based insurance has allowed these data models to be refined to measure more specific behaviors, such as an individual driver's frequency of sudden acceleration/deceleration, hard braking, distracted driving, or propensity for speeding. However, such aggregate methods lack contextual data, can ignore driver-specific behavior patterns or extrinsic factors that occur in particular scenarios, and lead to inaccuracies in driver risk assessment. SUMMARY The present disclosure describes determining personalized driver risk. In an implementation, a computer-implemented method for determining personalized driver risk, comprising: collecting, as collected data, location data and driving data associated with a one or more trips associated with a driver; identifying, based on the collected data, one or more risky behaviors of the driver; combining, as combined data, the collected data with mapping data associated with the location data; determining, based on the combined data, driver behavior data associated with the driver; and providing behavioral information associated with the driver and driving suggestion information on a computer display of a mobile computing device.. The described subject matter can be implemented using a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer-implemented system comprising one or more computer memory devices interoperably coupled with one or more computers and having tangible, non-transitory, machine-readable media storing instructions that, when executed by the one or more computers, perform the computer-implemented method/the computer-readable instructions stored on the non-transitory, computer-readable medium. The subject matter described in this specification can be implemented to realize one or more of the following advantages. First, the described approach provides contextual data, specific behavior patterns, and extrinsic factors that occur in particular scenarios with respect to determined driver behaviors of a driver. Second, the more accurate/improved data enhances customer satisfaction and a determination of driver risk. Third, the described approach can distinguish between location-, behavioral-, and road-pattern-based risk. Fourth, behavior modification feedback messages can also be transmitted to a driver to assist the driver in avoiding risky behavior. The details of one or more implementations of the subject matter of this specification are set forth in the Detailed Description, the Claims, and the accompanying drawings. Other features, aspects, and advantages of the subject matter will become apparent to those of ordinary skill in the art from the Detailed Description, the Claims, and the accompanying drawings. DESCRIPTION OF DRAWINGS FIG. 1 illustrates a computer-implemented system for determining personalized driver risk, according to an implementation of the present disclosure.FIG. 2 is a flowchart illustrating an example of a computer-implemented method for determining personalized driver risk, according to an implementation of the present disclosure.FIG. 3 is a block diagram illustrating an example of a computer-implemented system used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures, according to an implementation of the present disclosure. Like reference numbers and designations in the various drawings indicate like elements. DETAILED DESCRIPTION The following detailed description describes determining personalized driver risk and is presented to enable any person skilled in the art to make and use the disclosed subject matter in the context of one or more particular implementations. Various modifications, alterations, and permutations of the disclosed implementations can be made and will be readily apparent to those of ordinary skill in the art, and the general principles defined can be applied to other implementations and applications, without departing from the scope of the present disclosure. In some instances, one or more technical details that are unnecessary to obtain an understanding of the described subject matter and that are within the skill of one of ordinary skill in the art may be omitted so as to not obscure one or more described implementations. The present disclosure is not intended to be limited to the described or illustrated implementations, but to be accorded the widest scope consistent with the described principles and features. Traditional auto insurance is based on high-level statistical data models that differentiate an individual driver from a population of drivers, using data such as age,