The present disclosure provides a method for automatic control of a vehicle and a method for training lane change intention prediction network. The method includes receiving a plurality of types of vehicle traveling information of a target vehicle; inputting the plurality of types of vehicle traveling information of the target vehicle into a lane change intention prediction network, the lane change intention prediction network comprising a plurality of sub-networks; performing, through the sub-networks, feature extraction on the types of vehicle traveling information respectively, and outputting feature extraction results; performing feature fusion on the feature extraction results outputted by the sub-networks, and predicting a lane change intention of the target vehicle according to a feature fusion result; and updating an autonomous driving route of a current vehicle according to the lane change intention of the target vehicle.

A control unit for a vehicle is configured to determine, during an approaching manoeuvre of a distance controller and/or cruise controller of the vehicle towards a vehicle in front, whether or not the vehicle will overtake or can overtake the vehicle in front during the approaching manoeuvre. Furthermore, during the approaching manoeuvre, the control unit is configured to adjust a behaviour of the cruise controller and/or adaptive cruise controller of the vehicle on the basis of the above determination.

A motion prediction method of other vehicle computes, based on a behavior of a second vehicle running in an adjacent lane, a lane change probability indicating a probability of the second vehicle conducting a lane change in front of an own vehicle from the adjacent lane into an own lane in which the own vehicle is running and, with use of the lane change probability and a threshold value, predicts the lane change to be conducted by the second vehicle.

An apparatus may include a driving information input unit for receiving driving information generated while a vehicle travels, a steering angle location control unit for receiving a command steering angle for autonomous driving and a current motor steering angle of a driving motor and outputting an autonomous driving command through location control, and a motor-driven power steering control unit for driving the driving motor based on the autonomous driving command in an autonomous driving mode, determining whether a driver intervenes in steering, based on the driving information during the autonomous driving, computing a driver command according to the driver' steering based on a result of the determination, computing a compensation output between the autonomous driving command and the driver command by applying a weighting according to a steering angular speed, and making a mode transition from the autonomous driving mode to a driver mode while driving the driving motor.

A vehicle for predicting a risk of collision includes a controller configured to: calculate distances between the vehicle and left and right lines of a first lane, respectively, using a position of the vehicle and first lane width information of the first lane, calculate distances between the surrounding vehicle and left and right lines of a second lane, respectively, using a position of the surrounding vehicle and second lane width information of the second lane, calculate a second distance between the vehicle and the surrounding vehicle by reflecting at least one of the calculated distances between the vehicle and the left and right lines of the first lane or the calculated distances between the surrounding vehicle and the left and right lines of the second lane to a first distance, and predict a risk of collision between the vehicle and the surrounding vehicle based on the second distance.

A pedestrian collision prevention system of a vehicle including a detection unit to detect one or both of a fixed object and a pedestrian located in a vicinity of the vehicle; a prediction unit to predict a collision possibility with the pedestrian based on one or both of the fixed object and the pedestrian detected by the detection unit; and a control unit to set a detection sensitivity based on the collision possibility predicted by the prediction unit and to control one or both of generation of a warning signal and driving of the vehicle based on the detection sensitivity.

The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.

A vehicle guidance system provides a travel function for automated longitudinal guidance of a motor vehicle at a signaling unit. The vehicle guidance system is designed to determine an environmental data based distance from an upcoming first signaling unit on the basis of environmental data from one or more environmental sensors of the vehicle, and to determine a map data based distance from the upcoming first signaling unit on the basis of map data relating to a road network travelled by the vehicle. The vehicle guidance system is further designed to determine an estimated value of the distance from the first signaling unit on the basis of the environmental data based distance and on the basis of the map data based distance, and to bring about the automated longitudinal guidance of the vehicle at the first signaling unit on the basis of the estimated value of the distance from the first signaling unit.

This vehicle turning control device controls the turning characteristic of a vehicle having braking/driving sources capable of independently controlling a braking/driving torque for each wheel. The vehicle turning control device includes a yaw moment control device for controlling a yaw moment occurring in the vehicle, and a slip determination device for determining a road surface state from the angular velocity and the angular acceleration of the wheel and the vehicle speed. The yaw moment control device includes a control gain calculator for calculating a control gain, a target yaw rate calculator for calculating a target yaw rate from the vehicle speed, the steering angle, and the control gain, and a yaw moment calculator for calculating the braking/driving torque for each wheel in accordance with the target yaw rate. The control gain calculator calculates the control gain on the basis of a determination result of the slip determination device.

A vehicle driving control method depending on a baby mode, may include, when the baby mode is activated, receiving information on a state of a vehicle seat, correcting a center state of charge (SOC) value of a battery of the vehicle based on the information on the state of the vehicle seat, determining a state of a transmission of the vehicle, and performing regenerative brake and brake pedal stroke (BPS) scale/filtering correction control or an electric vehicle (EV) mode and accelerator position sensor (APS) scale/filtering correction control based on the state of the transmission of the vehicle and the state of the vehicle seat.