Disclosed is a system for vehicle control. The system analyzes a state of each controller of multiple controllers inside a vehicle based on information collected from the controllers, learns the state of the each controller, based on a state analysis result obtained by analyzing the state of the each controller, and determines an abnormal state for at least one of the controllers, based on the state analysis result for the each controller and a learning result obtained by learning the state of the each controller. The system also includes a vehicle controller that manages vehicle control rights, based on the abnormal state for the at least one of the one or more controllers.

Disclosed is a system for vehicle control. The system analyzes a state of each controller of multiple controllers inside a vehicle based on information collected from the controllers, learns the state of the each controller, based on a state analysis result obtained by analyzing the state of the each controller, and determines an abnormal state for at least one of the controllers, based on the state analysis result for the each controller and a learning result obtained by learning the state of the each controller. The system also includes a vehicle controller that manages vehicle control rights, based on the abnormal state for the at least one of the one or more controllers.

A model generation method includes specifying image coordinates that fall within a two-dimensional image obtained by capturing at least a mobile object and that correspond to at least one point among vertexes of a rectangular shape formed when an outer shape of the mobile object viewed from above is projected on a road, as a key point of the mobile object, and creating the two-dimensional image, to which information on the key point is added, as training data, and generating a machine learning model that outputs the key point from a two-dimensional image obtained by capturing at least a mobile object, by performing machine learning using the training data.

A model generation method includes specifying image coordinates that fall within a two-dimensional image obtained by capturing at least a mobile object and that correspond to at least one point among vertexes of a rectangular shape formed when an outer shape of the mobile object viewed from above is projected on a road, as a key point of the mobile object, and creating the two-dimensional image, to which information on the key point is added, as training data, and generating a machine learning model that outputs the key point from a two-dimensional image obtained by capturing at least a mobile object, by performing machine learning using the training data.

A vehicle with an emergency reporting function includes a processor, a vehicle communication device, a first calling device, and a vehicle outside occupant detector. The processor determines, by means of the vehicle outside occupant detector, whether an occupant who has been present inside the vehicle before an emergency is present inside the vehicle or outside the vehicle after the emergency. If the occupant is present inside the vehicle after the emergency, the processor uses the first calling device to enable a communication between an occupant inside the vehicle and an operator terminal. If the occupant is present outside the vehicle after the emergency, the processor additionally uses a second calling device to enable a communication between an occupant present outside or inside the vehicle and the operator terminal. The second calling device allows for a communication outside the vehicle.

A vehicle with an emergency reporting function includes a processor, a vehicle communication device, a first calling device, and a vehicle outside occupant detector. The processor determines, by means of the vehicle outside occupant detector, whether an occupant who has been present inside the vehicle before an emergency is present inside the vehicle or outside the vehicle after the emergency. If the occupant is present inside the vehicle after the emergency, the processor uses the first calling device to enable a communication between an occupant inside the vehicle and an operator terminal. If the occupant is present outside the vehicle after the emergency, the processor additionally uses a second calling device to enable a communication between an occupant present outside or inside the vehicle and the operator terminal. The second calling device allows for a communication outside the vehicle.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold. A velocity output register registers the estimated lateral velocity of the vehicle when the lateral acceleration error is smaller than the predefined threshold, and a sideslip calculator calculates a sideslip angle of the vehicle in real time from the registered lateral velocity of the vehicle and a vehicle longitudinal velocity.

A vehicle control method includes recognizing a periphery of a vehicle, a first vehicle and a second vehicle present in the periphery, setting a target area between the first vehicle and the second vehicle based on a reference position of a first type of the first vehicle present in a lane of a lane change destination and a reference position of the first type of the second vehicle behind the first vehicle and present in the lane, and controlling the vehicle to enter the target area set based on a reference position of a second type of the first vehicle and a reference position of the second type of the second vehicle.

Tailgate position management systems and methods are disclosed herein. An example method includes determining that a tailgate of a vehicle is in a down position, the tailgate comprising a tailgate camera, and selectively adjusting an automatic vehicle assistance feature of the vehicle based on the tailgate being in the down position.