An environmental information acquiring unit (11) to acquire environmental information on an environment around a mobile object, an action information acquiring unit (12) to acquire action information on an action of a driver of the mobile object, a calculation unit (13) to obtain control information for performing automated driving control of the mobile object on the basis of the environmental information acquired by the environmental information acquiring unit (11) and a machine learning model (18) that uses the environmental information as an input and outputs the control information, a contribution information determining unit (14) to determine contribution information having a high degree of contribution to the control information on the basis of the environmental information and the control information, a cognitive information calculating unit (15) to calculate cognitive information indicating a cognitive region of the driver in the environment around the mobile object on the basis of the action information and the environmental information, a specification unit (16) to specify unrecognized contribution information estimated not to be recognized by the driver on the basis of the contribution information and the cognitive information, and an information output control unit (17) to output driving assistance information necessary for driving assistance on the basis of the unrecognized contribution information specified by the specification unit (16) are provided.

A system for controlling an intelligent network vehicle is provided, and the system comprises a sensor group configured to obtain sensor information; a sensing and positioning module configured to obtain sensing information and positioning information based on the sensor information; a planning and control module configured to determine vehicle planning control information based on the sensing information and the positioning information; a safety control module configured to determine safety control information based on the sensing information and the positioning information; a function assessment module configured to determine a vehicle state assessment result; a risk assessment module configured to determine a risk assessment result; a logical arbitration module configured to determine vehicle execution information by arbitrating the vehicle planning control information and the safety control information; and an execution module configured to control the vehicle driving based on the vehicle execution information.

A method for controlling an autonomous, mobile robot which is designed to navigate independently in a robot deployment area, using sensors and a map. According to one embodiment, the method comprises detecting obstacles and calculating the position of detected obstacles based on measurement data received by the sensors, and controlling the robot to avoid a collision with a detected obstacle, the map comprising map data that represents at least one virtual blocked region which, during the control of the robot, is taken into account in the same way as an actual, detected obstacle.

A method of detecting a lane line based on lidar data can include detecting, by a processor, points each estimated as a lane line in a lidar data, performing, by the processor, an estimation operation of estimating parameters of a mathematical model using the detected points, and performing, by the processor, a setting operation of calculating distances between each of the detected points and the mathematical model in which the parameters are estimated and setting the calculated distances as scores. The method can further include performing, by the processor, a summation operation of summing the scores, and setting, by the processor, the mathematical model determined according to the summation score as a lane line.

A method for controlling autonomous driving in a vehicle capable of the autonomous driving may include collecting vehicle travel status information and system status information during the autonomous driving, sensing a failure based on the system status information, identifying normally controllable actuators when sensing the failure, determining a risk degree corresponding to the sensed failure based on the normally controllable actuator information and the vehicle travel status information, determining a safety state based on normally controllable actuator information and the risk degree, and determining a failure safety strategy corresponding to the safety state.

A system for user interaction with an automated device includes a control system configured to operate the device during an operating mode corresponding to a first state in which the control system automatically controls the device operation, and the operating mode prescribes that a user monitor the device operation during automated control. The control system is configured to allocate a time period for the device to transition to a temporary state in which automated control is maintained and the user is permitted to stop monitoring and perform a task unrelated to device operation. The system includes a user interaction system including a visual display configured to present trajectory information, an indication as to whether an area is conducive to putting the device in the temporary state, and time period allocation information, the user interaction system including an interface engageable by the user to manage scheduling of allocated time period(s).

There is provided a method for preventing car collision, the method comprising: obtaining information about a vehicle stopped in front of a preceding vehicle acquired by the preceding vehicle; calculating a deceleration amount for preventing a collision with the stopped vehicle based on the information about the stopped vehicle; predicting a preset event related to the preceding vehicle based on a change in the position of the preceding vehicle; and controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result.

An embodiment method of controlling a moving object includes checking profile information of a user who rides in the moving object or status information of the user, checking a degree of risk based on the profile information or the status information of the user, setting an operation mode of the moving object based on the degree of risk, and controlling movement of the moving object based on the operation mode.

Techniques are described to enable a vehicle, such as an autonomous vehicle, to steer and/or apply brakes on a road when a failure condition occurs. An example method for autonomous driving operation includes receiving a reduced set of location information that describes a location of the autonomous vehicle on a road; receiving a reduced set of trajectory information where the autonomous vehicle is expected to be driven; determining a driving path information where the autonomous vehicle is expected to be driven; and in response to determining an occurrence of a fault condition: sending a first instruction to cause the autonomous vehicle to steer the autonomous vehicle using at least the driving path information and the reduced set of location information, and sending a second instruction to cause the autonomous vehicle to apply brakes.

An object of the present invention is to provide a vehicle motion control device that generates a travel path capable of realizing comfortable ride comfort and high safety with a small physical quantity related to a vehicle behavior such as a longitudinal acceleration, a lateral acceleration, and a vertical acceleration generated when a vehicle passes or avoids with respect to a predetermined region such as unevenness on a course of a vehicle. Therefore, the vehicle motion control device includes a vehicle behavior prediction portion that predicts a physical quantity related to a vehicle behavior that occurs when a vehicle maintains a reference route toward a predetermined region on a course and a physical quantity related to a vehicle behavior that occurs when the vehicle shifts to an avoidance route for avoiding the predetermined region, and a path generation portion that generates a travel path defined by the reference route or the avoidance route in which the physical quantity is smaller than a defined value.