A vehicular trailer hitching assist system includes a rear backup camera disposed at a rear portion of a vehicle and a control disposed in the vehicle. A display device includes a video display screen operable to display video images for viewing by a driver of the vehicle. During a hitching maneuver event undertaken by the driver to hitch a trailer hitch of the vehicle to a trailer tongue of a trailer, and while the driver of the vehicle is maneuvering the vehicle to hitch the vehicle to the trailer, the control outputs video images derived from image data captured by the rear backup camera for display by the video display screen. At least one overlay overlays the displayed video images to guide the driver during the hitching maneuver. The at least one overlay aids in guiding connection of the trailer hitch of the vehicle to the trailer tongue of the trailer.

The disclosure relates to a method for training a first neural network, in particular for generating training data for at least one second neural network, using a controller, wherein measurement data ascertained by at least one surroundings sensor or artificially generated data of initially ten traffic scenarios is received, the received measurement data is fed to the first neural network as input data in order to train the first neural network, and the first neural network which is trained on the basis of the input data is used to generate data of traffic scenarios which differ from the initial traffic scenarios. Furthermore, the disclosure relates to a method for training at least one second neural network, to a controller, to a computer program, and to a machine-readable storage medium.

An information processing device according to the present disclosure includes: an acquisition unit that acquires a model having a structure of a neural network and input information input to the model; and a generation unit that generates basis information indicating a basis for an output of the model after the input information is input to the model based on state information indicating a state of the model after the input of the input information to the model.

At least one nearby vehicle may be extracted from a vehicle vicinity image collected by a sensor equipped in the target vehicle. Lane recognition information representing which one position a position of the extracted nearby vehicle corresponds to with respect to the target vehicle, and/or vehicle position information representing a relative distance from the target vehicle to the nearby vehicle may be identified. An attention degree of the nearby vehicle may be calculated based on a speed of the nearby vehicle calculated from the relative distance and a vehicle speed of the target vehicle, the vehicle position information of the nearby vehicle, the relative distance of the nearby vehicle, the speed of the target vehicle, or any combination thereof. An alarm for the nearby vehicle may be displayed on a screen according to the calculated attention.

Presented are intelligent electronic footwear and apparel with controller-automated features, methods for making/operating such footwear and apparel, and control systems for executing automated features of such footwear and apparel. A method for operating an intelligent electronic shoe (IES) includes receiving, e.g., via a controller through a wireless communications device from a GPS satellite service, location data of a user. The controller also receives, e.g., from a backend server-class computer or other remote computing node, location data for a target object or site, such as a virtual shoe hidden at a virtual spot. The controller retrieves or predicts path plan data including a derived route for traversing from the user's location to the target's location within a geographic area. The controller then transmits command signals to a navigation alert system mounted to the IES's shoe structure to output visual, audio, and/or tactile cues that guide the user along the derived route.

A method, an electronic device and a computer-readable storage medium for testing an autonomous driving system which relate to the technical field of autonomous driving are proposed. An embodiment for testing the autonomous driving system includes: obtaining scenario description information of a testing scenario; analyzing the scenario description information, and determining a scenario risk, a scenario probability and a scenario complexity corresponding to the testing scenario; obtaining a scenario weight of the testing scenario according to the scenario risk, scenario probability and scenario complexity; determining a test period corresponding to the scenario weight, where the test period is used for the autonomous driving system being tested in the testing scenario. The technical solution may reduce the testing pressure of the autonomous driving system and improve the testing efficiency of the autonomous driving system.

A display device included in a vehicle includes: a communication unit, a display; and a control unit. The communication unit can receive vehicle travel information. The processor can calculate a speed range based on the received vehicle driving information and control the display to display a graphic object representative of the calculated speed range on the display.

A vehicle position and velocity estimation based on camera and LIDAR data are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device; determining a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device; tracking a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generating tracking data; determining a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determining a velocity of the proximate object using the 3D position and the tracking data; and outputting the 3D position and velocity of the proximate object relative to the autonomous vehicle.

A technique for tracking objects includes: determining a set of detected measurements based on a received return signal; determining a group that includes a set of group measurements and a set of group tracks; creating a merged factor, including a merged set of track state hypotheses associated with a merged set of existing tracks including a first set of existing tracks and a second set of existing tracks, by calculating the cross-product of a first set of previous track state hypotheses and a second set of previous track state hypotheses; determining a first new factor and a second new factor; calculating a first set of new track state hypotheses for the first new factor based on a first subset of the group measurements; and calculating a second set of new track state hypotheses for the second new factor based on a second subset of the group measurements.

To obtain a rider-assistance system capable of providing a rider of a straddle-type vehicle with a sense of comfort and safety during a turn, and a control method for such a rider-assistance system.

The present invention provides the rider-assistance system that assists with driving by the rider of the straddle-type vehicle and includes a controller. The controller includes: an object identification section that identifies an object approaching a side of the straddle-type vehicle on the basis of output of a communication device that wirelessly receives information output from infrastructure equipment or another vehicle; a body position information acquisition section that acquires position information of at least a part of a body of the rider on the turning straddle-type vehicle; a collision possibility determination section that determines a collision possibility of the rider with the object identified by the object identification section on the basis of the position information acquired by the body position information acquisition section; and a safety operation performing section that causes the rider-assistance system to perform safety operation in the case where the collision possibility determination section determines that the collision possibility is high.