Personalized driving risk modeling and estimation system and methods

A risk-predictive modeling system for providing driving risk modeling and estimation, the system comprising:

• a vehicle telematics system comprising one or more sensors sensing vehicle telematics data from a vehicle associated with a driver, wherein the vehicle telematics data includes at least a speed of the vehicle;
• and
• a computing device comprising:
• a risk-predictive modeling processor and a non-transitory memory unit storing computer-executable instructions, which when executed by the risk-predictive modeling processor, cause the computing device to:
• receive the vehicle telematics data from the vehicle telematics system;
• calculate and determine a driving behavior score dataset from the vehicle telematics data;
• calculate and determine a risk-exposure score dataset and a risk map from a historical database storing accident information, traffic data, vehicle volume data, vehicle density data, road characteristic data, or weather data;
• calculate and determine a driver-risk score dataset based on a weighted average of the driving behavior score dataset and the risk-exposure score dataset;
• calculate and determine, using weighting and machine learning, a driver score dataset associated with the driving behavior score dataset, the risk-exposure score dataset, the driver-risk score dataset, and a driver characteristics dataset;
• calculate, in near real-time and based on the vehicle telematics data, a level of frustration associated with the driver;
• calculate and determine a road frustration index dataset from the level of frustration of the driver;
• calculate and determine, using weighting and machine learning, a real-time driver score dataset associated with a current driving behaviors dataset, and the driver score dataset;
• calculate and determine, using weighting and machine learning, a driver-contextual risk score dataset associated with a contextual information dataset, the road frustration index dataset, and the real-time driver score dataset;
• calculate and determine a contextual response score dataset that includes scene-configuration information and contextual information of a scene;
• calculate and determine, using weighting and machine learning, a risk-response readiness dataset associated with the driver-risk score dataset, the driver-contextual risk score dataset, and the road frustration index dataset;
• calculate and determine, using weighting and machine learning, a conflict index dataset associated with the contextual response score dataset and the risk-response readiness dataset;
• and
• calculate and determine an overall driving risk index value using the risk-predictive modeling system that includes the driver score dataset, the driver-contextual risk score dataset, and the conflict index dataset.