An information processing apparatus compares a sense of use of a first vehicle with a sense of use of a second vehicle based on first information about one or more automobile components used by a user in the first vehicle that the user has used in the past in the first vehicle and second information about one or more automobile components used by the user in the second vehicle that is a vehicle candidate lent to the user. Then, the information processing apparatus notifies the user of result information related to a result of the comparing the sense of use of the first vehicle with the sense of use of the second vehicle.

A system including a proximity monitor associated with an entity. The proximity monitor includes a communication interface, a memory, and an electronic processor. The communication interface receives first position state data and identification data associated with a neighboring entity. The electronic processor includes instructions to associate a first shape model with the neighboring entity based on the identification data, determine a first space occupied by the neighboring entity based on the first position state data and the first shape model, identify a proximity event based on the first space occupied by the neighboring entity relative to a position of the entity, and generate an alert based on the proximity event.

A system including a proximity monitor associated with an entity. The proximity monitor includes a communication interface, a memory, and an electronic processor. The communication interface receives first position state data and identification data associated with a neighboring entity. The electronic processor includes instructions to associate a first shape model with the neighboring entity based on the identification data, determine a first space occupied by the neighboring entity based on the first position state data and the first shape model, identify a proximity event based on the first space occupied by the neighboring entity relative to a position of the entity, and generate an alert based on the proximity event.

Provided is a driver assistance system (DAS) including: a storage configured to store driver tendency data including an amount of decrease in acceleration when a vehicle approaches a specific section, in a state in which an adaptive cruise control (ACC) of the vehicle is deactivated; and a controller including at least one processor configured to process the driver tendency data, wherein the controller is configured to, upon the vehicle re-entering the specific section during travel of the vehicle with the ACC, control the vehicle to perform one of acceleration control and deceleration control based on the driver tendency data.

Provided is a driver assistance system (DAS) including: a storage configured to store driver tendency data including an amount of decrease in acceleration when a vehicle approaches a specific section, in a state in which an adaptive cruise control (ACC) of the vehicle is deactivated; and a controller including at least one processor configured to process the driver tendency data, wherein the controller is configured to, upon the vehicle re-entering the specific section during travel of the vehicle with the ACC, control the vehicle to perform one of acceleration control and deceleration control based on the driver tendency data.

A method and a system analyze the control of a vehicle having an autonomous driving unit. A change in the driving mode from autonomous driving to manual driving is detected, and at least one driving parameter before and/or after detecting the change is monitored. Based on driving values obtained by the monitoring with respect to the detected change in driving mode, at least one driving quantity quantifying the quality of interplay between the autonomous driving unit and a human driver is determined.

Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

Determining that a motor vehicle driver is using a mobile device while driving a motor vehicle. Multiple images of a driver of a motor vehicle are captured through a side window of the motor vehicle. Positive images show a driver using a mobile device while driving a motor vehicle. Negative images show a driver not using a mobile device while driving a motor vehicle. Multiple training images are selected from both the positive images and the negative images. The selected training images and respective labels, indicating that the selected training images are positive images or negative images, are input to a machine (e.g. Convolutional Neural Network, (CNN)). The CNN is trained to classify that a test image, captured through a side window of a motor vehicle, shows a driver using a mobile device while driving the motor vehicle.

A reward point management device includes one or more processors configured to: calculate a safe driving score based on safe driving points corresponding to a degree of safe driving of a user according to a user's driving behavior in a leased vehicle; calculate an eco-driving score based on eco-driving points corresponding to a degree of eco-driving of the user according to the user's driving behavior in the leased vehicle; calculate a service score based on service entry information on the leased vehicle; and calculate reward points to be given to the user based on the safe driving score, the eco-driving score, and the service score.

A reward point management device includes one or more processors configured to: calculate a safe driving score based on safe driving points corresponding to a degree of safe driving of a user according to a user's driving behavior in a leased vehicle; calculate an eco-driving score based on eco-driving points corresponding to a degree of eco-driving of the user according to the user's driving behavior in the leased vehicle; calculate a service score based on service entry information on the leased vehicle; and calculate reward points to be given to the user based on the safe driving score, the eco-driving score, and the service score.