A system for automatically closing a trunk lid of a vehicle. The system comprises a trunk sensor system configured for detecting items in a luggage trunk, a trunk lid actuator for automatic closing of the trunk lid, and a control unit connected to the trunk sensor system and to the trunk lid actuator. The control unit is arranged for statistically determining, based on a set of observations of a trunk interior space by means of the trunk sensor system in connection with termination of a set of vehicle travels, one or more items or combination of items that are frequently remaining in the luggage trunk after completed travel, while preferably taking into account the one or more items stored in the luggage trunk during the travel, detecting opening of the trunk lid, detecting current items in the luggage trunk by means of the trunk sensor system, and controlling the trunk lid actuator for automatic closing of the trunk lid when the current items correspond to said one or more items or combination of items that are frequently remaining in the luggage trunk after a journey. The disclosure also relates to a corresponding computer-implemented method for automatically closing a trunk lid of a vehicle luggage trunk.

A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.

An apparatus of controlling a safety power window of a vehicle and a method thereof to not injure the body caught in a window frame, as well as to prevent a malfunction of the safety power window, by adaptively setting an operation reference value of the safety power window, may include a learning device that deep-learns a model which predicts an operation reference value of the safety power window provided in the vehicle, based on driving information of the vehicle, a sensor that collects the driving information of the vehicle, and a controller that obtains the operation reference value of the safety power window corresponding to current driving information of the vehicle by use of the model on which the deep learning is completed, and controls the safety power window according to the operation reference value of the safety power window.

A sectional door operator system (1) for opening and closing an opening (2) is provided herein. The sectional door operator system (1) comprises a door (8) arranged to be moved between an open (O) and closed (C) position and comprising a plurality of horizontal and interconnected sections (9a-e), and a door frame (3) comprising a first frame section (4) at a first side (5) of the opening (2) and a second frame section (6) at a second side (7) of the opening (2), wherein the plurality of horizontal and interconnected sections (9a-e) are connected to the door frame (3). The sectional door operator system (1) further comprises a drive unit system (100) mounted on a section (9e) of the plurality of horizontal and interconnected sections (9a-e), wherein the drive unit system (100) is arranged to move the sectional door (8) from the closed position (C) to the open position (O), wherein the drive unit system (100) comprises at least a first drive unit (10a) comprising a first motor (11a) and at least a second drive unit (10b) comprising a second motor (11b), and wherein the first drive unit (10a) and the second drive unit (10b) are mounted at different vertical sides of the horizontal and interconnected section (9e). Further, the sectional door operator system (1) comprises at least one sensor device (40a, 40b) mounted on a section (9e) of the plurality of horizontal and interconnected sections (9a-e), and at least one control unit (20a, 20b) being in operative communication with the drive unit system (100) and configured to control the operation of the drive unit system (100) at least based on sensor data (42) from the at least one sensor device (40a, 40b), wherein the sensor data (42) relates to an angle (φ) of the door (8) in relation to a true horizontal plane of the sectional door operator system (1).

Tubular drive apparatus for a panel of a vehicle, preferably a tailgate or door, comprising two tubes which are inserted into one another in a telescopic manner, the free ends of which are each hinged to the panel of the vehicle and to the vehicle itself, the tubes being capable of being separated from one another and brought together by means of a spindle which is driven by an electric motor and comprises a spindle nut arranged thereon, in order to thus open and close the panel, the electric motor, the spindle, the spindle nut and an electronic circuit board comprising sensor elements being arranged within the tubular drive apparatus, and a power electronics unit for actuating the electric motor also being arranged on the electronic circuit board.

A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.

Systems, methods and computer readable media for controlling vehicles in response to windows are provided. One or more images captured using one or more image sensors from an environment of a vehicle may be obtained. The one or more images may be analyzed to detect a first window in the environment. The vehicle may be navigated to a stopping position, where in the stopping position a window of the vehicle may be positioned at a selected position with respect to the first window.

An electronic door system is disclosed for a door that selectively closes a door opening of a building structure. The electronic door system includes two or more of a gyroscope that senses angular velocity of the door, an accelerometer that senses acceleration of the door, a capacitive sensor that capacitively senses the building structure, or a microphone that senses sound of the door. The electronic door system also includes a controller that assesses a physical position of the door according to the two or more of the gyroscope, the accelerometer, the capacitive sensor, or the microphone.

Method and apparatus are disclosed for park-assist based on vehicle door open positions. An example vehicle includes a door, a door sensor, a range-detection sensor, and a controller. The controller is to determine, via the door sensor, a preferred angle of an occupant for opening the door and detect, via the range-detection sensor, a spot that is unoccupied. The controller also is to predict a maximum angle for opening the door within the spot. The example vehicle also includes an autonomy unit to perform park-assist into the spot responsive to the maximum angle equaling or exceeding the preferred angle.

Method and apparatus are disclosed for park-assist based on vehicle door open positions. An example vehicle includes a door, a door sensor, a range-detection sensor, and a controller. The controller is to determine, via the door sensor, a preferred angle of an occupant for opening the door and detect, via the range-detection sensor, a spot that is unoccupied. The controller also is to predict a maximum angle for opening the door within the spot. The example vehicle also includes an autonomy unit to perform park-assist into the spot responsive to the maximum angle equaling or exceeding the preferred angle.