Systems and methods for assessing driving risk

A method for assessing driving risk, the method comprising operating at least one processor to:

• receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles, the telematics data comprising at least geospatial data;
• determine, using the geospatial data, a velocity vector, a longitudinal acceleration, and a lateral acceleration for at least one vehicle of the plurality of vehicles during a trip;
• identify, for each trip, at least one trip subinterval, each trip subinterval comprising a non-zero speed event having the velocity vector, the longitudinal acceleration, and the lateral acceleration associated therewith;
• identify, for each trip subinterval, an anomalous trip event by:
• identifying a plurality of trip events by applying to each trip subinterval a stochastic model, and
• determining that at least one trip event of the plurality of trip events is anomalous based on the at least one trip event exceeding a predetermined threshold;
• and
• determining a trip risk score based at least in part on a proportion of anomalous trip events.

A non-transitory computer-readable medium having instructions stored thereon executable by at least one processor to implement a method for assessing driving risk, the method comprising operating at least one processor to:

• receive telematics data originating from a plurality of telematics devices installed in a plurality of vehicles, the telematics data comprising at least geospatial data;
• determine, using the geospatial data, a velocity vector, a longitudinal acceleration, and a lateral acceleration for at least one vehicle of the plurality of vehicles during a trip;
• identify, for each trip, at least one trip subinterval, each trip subinterval comprising a non-zero speed event having the velocity vector, the longitudinal acceleration, and the lateral acceleration associated therewith;
• identify, for each trip subinterval, an anomalous trip event by:
• identifying a plurality of trip events by applying to each trip subinterval a stochastic model, and
• determining that at least one trip event of the plurality of trip events is anomalous based on the at least one trip event exceeding a predetermined threshold;
• and
• determining a trip risk score based at least in part on a proportion of anomalous trip events.