Patent Application Titled “Method of predicting the future accident risk rate of the drivers using artificial intelligence and its device” Published Online (USPTO 20230267549): Patent Application

2023 SEP 12 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor LEE, Eunsu (Seoul, KR), filed on August 10, 2022, was made available online on August 24, 2023.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “

“Field of the Invention

“The present invention relates to a method of predicting the future accident risk rate of the drivers using artificial intelligence such as machine learning or deep learning, and its device, and more particularly, the method of predicting the future accident risk rate of the drivers using artificial intelligence and its device such as machine learning or deep learning based on vehicle driving habit data collected from GPS, IMU sensor, and vision sensor, and its device.

“Background Technology Description of the Related Art

“In car insurance, it is very important to estimate the probability that a driver will cause an accident and the amount of compensation accordingly. This is directly related to the profit of the insurance product, and if the forecast is wrong, a loss may occur.

“Specifically, in car insurance, the loss ratio, which is the value obtained by dividing the cost of insurance, such as insurance money, by the insurance premium received from the insured, is used as an index. In order to lower the loss ratio, insurance companies find drivers with high accident risk and either charge high insurance premiums or refuse to take over. Drivers with a low risk of accidents can be encouraged to sign up by lowering insurance premiums or providing incentives.

“In order to manage the loss ratio, insurance companies are using each driver’s personal information, vehicle type, and accident history to determine insurance premiums for each driver.

“Republic of Korea Patent No. 2318801, “Driver’s Traffic Accident Rate Prediction System” discloses a driver’s traffic accident prediction system that predicts the accident rate that may occur in the future for each driver based on personal information, violation information related to past driving, and accident information.

“However, it is difficult to accurately grasp the unique driving habits of each driver and the risk factors inherent in these driving habits from a simple accident history.

“Therefore, in recent years, various attempts have been made to collect information on the driver’s driving habits, perform an analysis based on this information, and to objectify potential risk factors inherent in the driver’s driving habits as a basis for calculating insurance premiums.

“There is a prior art that collects driving habit data such as speeding, rapid acceleration, and deceleration using a driving collection terminal such as GPS and OBD (On-Board Diagnostic). For example, there are T-map driving score-linked discount and Carrot Insurance’s mileage-based per mile insurance products in Republic of Korea, which is called UBI (Usage Based Insurance). However, the current UBI on the market uses only data such as speeding/rapid acceleration/deceleration, which are information that does not take into account the driving context, so it is often difficult to derive accurate driving habits. For example, in the case of a sudden deceleration, it is difficult to determine whether the sudden deceleration is due to the risk of an accident or whether it is a simple driving habit unique to the driver, so it is difficult to derive a correlation with an actual traffic accident.

“In order to improve this situation, the insurance industry proposes a driving habit-based insurance BBI (Behavior Based Insurance) product as a next-level technology product for UBI. An example of such a BBI is Tesla’s driving habit-based insurance.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “The present invention has been devised in view of the above-described problems, and its purpose is providing the method of predicting the future accident risk rate of the drivers using artificial intelligence and its device that can be used for insurance premium calculations by analyzing the driver’s driving propensity.

“Device of predicting the future accident risk rate of the drivers using artificial intelligence of the present invention for solving the above problems includes: a driving habit data collection device comprising a driving habit data collection unit that has built-in GPS, IMU sensor, and a vision sensor to collect vehicle driving information per trip, and a CPU that manages the collection of driving habit data; a driving habit data storage server for storing driving habit data collected from the driving habit data collection unit; and a main server including a main database for receiving the driving habit data of the driving habit data storage server, a data pre-processing unit for pre-processing the driving habit data of the main database for each variable, an artificial intelligence model that predicts the accident risk of vehicle driving by inputting the data preprocessed in the data pre-processing unit, an accident risk database that stores the accident risk output from the artificial intelligence model, and a control unit that manages the accident risk prediction.

“Preferably, each driving habit data pre-processed for each variable includes longitude, latitude, and altitude from GPS; accelerations in the x, y, and z-axis directions (ax, ay, az) from the IMU and angular accelerations in the x, y, and z-axis directions (gx, gy, gz); and distance from the vision sensor to the front vehicle(front_distance), the speed of the front vehicle (front_speed), the bias of the subject vehicle in the center of the lane (bias), the estimated time until collision with the front vehicle (ttc).

“Preferably, further comprising an insurance server that differentiates car insurance premiums for each driver based on the accident risk that is the output value of the artificial intelligence model from the main server.

“Preferably, a trip, which is a driving unit, is defined as the time from turning on the ignition of the vehicle to ending the starting. The vehicle driving data collected by the driving habit data collection device has a configuration including all variables per one trip, the data pre-processing unit stores the data of each sensor value as a time frame once at a predetermined time so that it is easier to handle the driving habit data, and all files corresponding to the same sensor value are merged and stored as one file.

“Preferably, the data pre-processing unit performs feature engineering, in the feature engineering, driving habit data is stored as an average value once at a predetermined time.

“Preferably, the artificial intelligence model is any one selected from random forest, XGBoost, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN).

“The method of predicting the future accident risk rate of the drivers using artificial intelligence of the present invention for solving the above other problems includes: collecting driving habit data from the driving habit data collection device through a driving habit data collection unit having a GPS, IMU sensor, and vision sensor; storing the driving habit data collected by the driving habit data collection device in a driving habit data storage server; transmitting the driving habit data stored in the driving habit data storage server to the main database of the main server; performing a pre-processing of driving habit data in the data pre-processing unit of the main server; obtaining an output value by inputting preprocessed data into the artificial intelligence model of the main server; and storing the output value of the artificial intelligence model of the main server in an accident risk database, and predicting the accident risk of the driver’s vehicle using the output value.

“Preferably, the pre-processing in the data pre-processing unit of the main server is, further comprising a generating processed data by performing a feature engineering of extracting features by using domain knowledge of driving habit data in order to apply them to the artificial intelligence model.

“According to the present invention having the above-described configuration, vehicle driving habit data is applied to an artificial intelligence model to predict an individual driver’s accident risk, and this accident risk can be used for insurance premium calculation and the like.

“In addition, before applying to the AI model, it is possible to pre-process the variables of the vehicle driving habit data to make it easier to handle in the AI model.

“Since there is a limit of memory to take all of the driving time of drivers into account, data corresponding to a time frame of a certain time is stored as a median value rather than an average value. It is possible to alleviate the bouncing data numerical values due to the smoothing effect that occurs when the average value is stored.

“In addition, the vehicle driving habit data collected in the present invention uses a driving habit data collection device separately installed in the vehicle equipped with a Global Positioning System (GPS), an Inertial Measurement Unit (IMU) sensor, and a vision sensor. In particular, the data collected through the camera of the vision sensor, such as the speed of the front vehicle, the distance between the front vehicle and subject vehicle, the estimated time it takes to collide with the front vehicle, and the degree of deviation of the subject vehicle from the center of the lane, are contextual data and are actually related to a traffic accident. The prior art using a driving collection terminal such as GPS and OBD (On-Board Diagnostic) does not use a camera, so it is impossible to secure driving contextual data as described above.

“In addition, the driving habit data collection device is recognized individually, and through this, it is possible to predict the accident risk of an individual vehicle or driver.”

The claims supplied by the inventors are:

“1. A device of predicting the future accident risk rate of the drivers using artificial intelligence, comprising: a driving habit data collection device comprising a driving habit data collection unit that has built-in GPS, IMU sensor, and a vision sensor to collect vehicle driving information per trip, and a CPU that manages the collection of driving habit data; a driving habit data storage server for storing driving habit data collected from the driving habit data collection unit; and a main server including a main database for receiving the driving habit data of the driving habit data storage server, a data pre-processing unit for pre-processing the driving habit data of the main database for each variable, an artificial intelligence model that predicts the accident risk of vehicle driving by inputting the data preprocessed in the data pre-processing unit, an accident risk database that stores the accident risk output from the artificial intelligence model, and a control unit that manages the accident risk prediction.

“2. A device of predicting the future accident risk rate of the drivers using artificial intelligence of claim 1, wherein each driving habit data pre-processed for each variable is longitude, latitude, and altitude from GPS; accelerations in the x, y, and z-axis directions (ax, ay, az) from the IMU and angular accelerations in the x, y, and z-axis directions (gx, gy, gz); and distance from the vision sensor to the front vehicle(front_distance), the speed of the front vehicle (front_speed), the bias of the subject vehicle in the center of the lane (bias), the estimated time until collision with the front vehicle (ttc).

“3. A device of predicting the future accident risk rate of the drivers using artificial intelligence of claim 1, wherein further comprising an insurance server that differentiates car insurance premiums for each driver based on the accident risk that is the output value of the artificial intelligence model from the main server.

“4. A device of predicting the future accident risk rate of the drivers using artificial intelligence of claim 1, wherein the vehicle driving data collected by the driving habit data collection device has a configuration including all variables per one trip, the data pre-processing unit stores the data of each sensor value as a time frame once at a predetermined time so that it is easier to handle the driving habit data, and all files corresponding to the same sensor value are merged and stored as one file.

“5. A device of predicting the future accident risk rate of the drivers using artificial intelligence of claim 1, wherein the data pre-processing unit performs feature engineering, in the feature engineering, driving habit data is stored as an average value once at a predetermined time.

“6. A device of predicting the future accident risk rate of the drivers using artificial intelligence of claim 1, wherein the artificial intelligence model is any one selected from random forest, XGBoost, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN).

“7. A method of predicting the future accident risk rate of the drivers using artificial intelligence, comprising: collecting driving habit data from the driving habit data collection device through a driving habit data collection unit having a GPS, IMU sensor, and vision sensor; storing the driving habit data collected by the driving habit data collection device in a driving habit data storage server; transmitting the driving habit data stored in the driving habit data storage server to the main database of the main server; performing a pre-processing of driving habit data in the data pre-processing unit of the main server; obtaining an output value by inputting preprocessed data into the artificial intelligence model of the main server; and storing the output value of the artificial intelligence model of the main server in an accident risk database, and predicting the accident risk of the driver’s vehicle using the output value.

“8. A method of predicting the future accident risk rate of the drivers using artificial intelligence in claim 7, wherein the pre-processing in the data pre-processing unit of the main server is, further comprising a generating processed data by performing a feature engineering of extracting features by using domain knowledge of driving habit data in order to apply them to the artificial intelligence model.”

For more information, see this patent application: LEE, Eunsu. Method of predicting the future accident risk rate of the drivers using artificial intelligence and its device. U.S. Patent Application Number 20230267549, filed August 10, 2022 and posted August 24, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20230267549)&db=US-PGPUB&type=ids

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