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 vehicular vision system includes an electronic control unit (ECU) disposed at a vehicle and including an image processor that processes image data captured by at least one camera of the vehicle. An under-trailer camera is disposed at an underside of a trailer hitched to the vehicle. The under-trailer camera has a field of view under the trailer that includes a ground surface under the trailer that is traveled over by the trailer. The under-trailer camera captures image data as the vehicle and trailer are moving. The ECU, responsive to processing of image data captured by the under-trailer camera, detects an object under the trailer and determines that the detected object under the trailer is greater than a threshold size. The ECU, responsive to determination that the detected object under the trailer is greater than the threshold size, generates an output.

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 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.

The traffic flow control method includes: receiving, by a traffic flow control device, traffic control request signaling sent by an in-vehicle device of a first vehicle, where the traffic control request signaling includes travel information of the first vehicle and a travel intention of the first vehicle; determining, by the traffic flow control device, traffic command signaling based on the traffic control request signaling and traffic control phase information of a target intersection, where the target intersection is an intersection through which the first vehicle is to pass; and sending, by the traffic flow control device, the traffic command signaling to the in-vehicle device of the first vehicle. The traffic flow control method, the traffic flow control device, the in-vehicle device, and the computer-readable storage medium in the internet of vehicles can help a vehicle in the internet of vehicles travel safely and efficiently at an intersection.

An apparatus for displaying a virtual lane may include: a processor to generate the virtual lane by converting lane information of a preceding vehicle into lane information viewed in a viewpoint of a host vehicle, in platooning, and a display to display the virtual lane.

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.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.

Provided is an object recognition device including a prediction processing unit, a temporary setting unit, and a association processing unit. The prediction processing unit predicts, as a prediction position on an object model obtained by modeling a tracking target, a position of a movement destination of the tracking target based on a trajectory formed by movement of at least one object of a plurality of objects as the tracking target. The temporary setting unit sets, based on specifications of a sensor that has detected the tracking target, a position of at least one candidate point on the object model. The association processing unit sets, based on the position of the candidate point and the prediction position, a reference position on the object model. The association processing unit determines whether the position of the detection point and the prediction position associate with each other based on a positional relationship between a association range which is set so that the association range has a reference position on the object model as a reference and a detection point at a time when the sensor has detected the at least one object of the plurality of objects.

Provided is an object recognition device including a prediction processing unit, a temporary setting unit, and a association processing unit. The prediction processing unit predicts, as a prediction position on an object model obtained by modeling a tracking target, a position of a movement destination of the tracking target based on a trajectory formed by movement of at least one object of a plurality of objects as the tracking target. The temporary setting unit sets, based on specifications of a sensor that has detected the tracking target, a position of at least one candidate point on the object model. The association processing unit sets, based on the position of the candidate point and the prediction position, a reference position on the object model. The association processing unit determines whether the position of the detection point and the prediction position associate with each other based on a positional relationship between a association range which is set so that the association range has a reference position on the object model as a reference and a detection point at a time when the sensor has detected the at least one object of the plurality of objects.