Patent ID: 11934985
Assignee: V3 SMART TECHNOLOGIES PTE LTD
Field: Computer technology (Electrical engineering)
Classification: CPC G  B | IPC B  G

Claim 11:
12. A method for evaluating driving risk, comprising:
receiving data from a vehicle, the data comprising GPS data including vehicle location and speed data, acceleration data and image data, wherein the image data comprises external view image data of external views of a surrounding environment outside the vehicle and internal view image data comprising an in-cabin view inside the vehicle, the external view image data comprising images of road conditions, images of traffic conditions, images of weather conditions, images of lighting conditions and images of other vehicles in the surrounding environment outside the vehicle, and the internal view image data comprising certain driver image data related to a certain driver and driver posture and movement data comprising driver head movement data, driver hand movement data, and driver eye movement data;
training a situation classification model based on the external image data, the GPS data, and the acceleration data received from the vehicle, and based on previous data received from the vehicle and/or other vehicle using machine learning methods to classify various driving situations;
training a maneuver classification model based on the GPS data, the acceleration data, and the driver posture and movement data, and based on previous data received from the vehicle and/or other vehicles using machine learning methods to classify various driving maneuvers;
thereafter identifying a plurality of risks associated with a certain driver based on the data received from the vehicle including the internal view image view data captured by the vehicle internal view camera when each of the plurality of risks occurs identifying a driver of the vehicle as the certain driver, one or more of the various driving situations classified by the situation classification model and one or more of the driving maneuvers classified by the maneuver classification model;
determining a plurality of weights associated with the certain driver, wherein a respective weights is assigned for each of the plurality of risks; and
generating a score for the certain driver based on the plurality of weights for the plurality of risks associated with the certain driver.