Method and system for providing carbon offset sources. For example, the method includes determining an amount of total carbon emission of a user, receiving a desired percentage of carbon offset, determining a total number of carbon offset units corresponding to a predetermined carbon offset source, providing multiple carbon offset sources, receiving a respective number of carbon offset units corresponding to the predetermined carbon offset source for each of the multiple carbon offset sources, determining a respective cost for each of the multiple carbon offset sources, and providing a total amount of cost based upon the respective cost for each of the multiple carbon offset sources, where a total of the respective number of carbon offset units is equal to the total number of carbon offset units.

To implement a configuration to calculate a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a requested recovery ratio (RRR) for each road section, and issue a manual driving recovery request notification on the basis of the calculated time. A data processing unit is included, which calculates a manual driving recoverable time required for a driver who is executing automatic driving in order to achieve a predefined requested recovery ratio (RRR) from automatic driving to manual driving and determines notification timing of a manual driving recovery request notification on the basis of the calculated time. The data processing unit acquires the requested recovery ratio (RRR) for each road section set as ancillary information of a local dynamic map (LDM), and calculates the manual driving recoverable time for each road section scheduled to travel, using learning data for each driver.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

Included are: a captured image acquiring unit to acquire a captured image obtained by imaging an occupant; a temperature image acquiring unit to acquire a temperature image indicating a temperature of a surface of a body of the occupant measured in a non-contact manner; a motion detection unit to detect a motion of the occupant on the basis of the captured image; a temperature detection unit to detect a temperature of a hand of the occupant on the basis of the temperature image; and an awakening level estimating unit to estimate an awakening level of the occupant on the basis of the motion of the occupant detected by the motion detection unit and the temperature of the hand of the occupant detected by the temperature detection unit.

Included are: a captured image acquiring unit to acquire a captured image obtained by imaging an occupant; a temperature image acquiring unit to acquire a temperature image indicating a temperature of a surface of a body of the occupant measured in a non-contact manner; a motion detection unit to detect a motion of the occupant on the basis of the captured image; a temperature detection unit to detect a temperature of a hand of the occupant on the basis of the temperature image; and an awakening level estimating unit to estimate an awakening level of the occupant on the basis of the motion of the occupant detected by the motion detection unit and the temperature of the hand of the occupant detected by the temperature detection unit.

The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

A method of controlling a vehicle using a first model trained according to optimized output of a second model.

Methods and systems are described for executing vehicle functions. The system includes a sensor, an input device, and a controller that is configured to adjust a plurality of vehicle functions based on user identification. The controller detects a first user input received at the input device and identifies a first user based on a first signal received from the sensor, the first signal corresponding to a first vehicle function. The controller adjusts the first vehicle function corresponding to the first signal and detects a second user input received at the input device. Additionally, the controller identifies a second user based on a second signal received from the sensor, the second signal corresponding to a second vehicle function. The controller adjusts the second vehicle function corresponding to the second signal. The first vehicle function and the second vehicle function perform different vehicle functions.

Methods and systems are described for executing vehicle functions. The system includes a sensor, an input device, and a controller that is configured to adjust a plurality of vehicle functions based on user identification. The controller detects a first user input received at the input device and identifies a first user based on a first signal received from the sensor, the first signal corresponding to a first vehicle function. The controller adjusts the first vehicle function corresponding to the first signal and detects a second user input received at the input device. Additionally, the controller identifies a second user based on a second signal received from the sensor, the second signal corresponding to a second vehicle function. The controller adjusts the second vehicle function corresponding to the second signal. The first vehicle function and the second vehicle function perform different vehicle functions.