An approach for reducing transmission of probe car data over a network is provided. The approach includes using a processor to receive, at one or more processors remote from a vehicle, a first set of probe car data for the vehicle, wherein the first set of probe car data comprises a trigger event from a first time. The processor determine that no additional set of probe car data is received during a second time interval. The processor also determines that a trigger event has not occurred during the second time interval based on the determination that no additional set of probe car data was received. The processor also estimates an estimated probe car data for the vehicle at the second time interval based on the first set of probe car data and a non-trigger assumption.

A system for operation of an autonomous transportation network and a method of operation for a plurality of multi-featured autonomous vehicles are disclosed. The system comprises a road, a control management center and a plurality of multi-featured autonomous vehicles. The multi-featured autonomous vehicles include different types of vehicles for transportation of passengers or goods in the autonomous transportation network. The method of operation disclosed comprises selecting permissible routes from an origin to a destination for the multi-featured autonomous vehicles and predicting conflicts for the multi-featured autonomous vehicles. Conflict avoidance instructions are generated and are transmitted to the multi-featured autonomous vehicles using infrastructure elements. The method of operation comprises adjusting the route of the multi-featured autonomous vehicles (20) using corrected route instructions calculated by an onboard processor of the multi-featured autonomous vehicles.

A method, a central control system and a non-transitory computer-readable storage medium are provided for determining driving assisting data in relation to a vehicle based on sensor data and map data in relation to other vehicles. Sensor data are obtained from a sensor system of a first vehicle, the sensor data comprising a pose and a velocity of a second vehicle located in a surrounding environment of the first vehicle. Furthermore, map data are obtained comprising a road geometry of the surrounding environment of the first vehicle. The pose and the velocity of the second vehicle are compared with the map data, and driving assisting data in relation to a third vehicle are determined based on the comparison.

A parking recognition server of a personal mobility device and a method thereof are provided. The parking recognition server includes a processor configured to determine whether a personal mobility device is parked in a parking area, and a storage configured to store data and an algorithm run by the processor and a usage history for each user who uses the personal mobility device. The processor may be further configured to predict a destination and a movement path of a user for the usage history for each user, and determine a device candidate group having a high probability of being parked in the predicted destination based on a degree of overlap between the predicted movement path and an actual movement path of the user.

A system and method are for determining a vehicle speed. In an embodiment, the system includes a first collecting module, adapted to collect traffic information; and a deep learning module, including a control model and a neural network. The control model is generated after the neural network autonomously deeply learns based on inputted traffic information. The control model is adapted to receive current traffic information including a speed and a location of a vehicle, traffic light status at a junction ahead, and traffic condition information in a road segment ahead currently collected by the first collecting module; to compute a recommended vehicle speed for the vehicle based on the current traffic information; and to output the recommended vehicle speed. Such a system may reduce the stopping times at the intersection.

The present invention concerns a vehicle tracking device for tracking one or more vehicles at a geographic location of a ransport network within which the one or more vehicles are able to move, the vehicle tracking device comprising: one or more infra-red (IR) sensors having a field of view and being configured to detect IR radiation being emitted from or reflected by the one or more vehicles at the geographic location within the field of view; a receiver configured to receive unique identification data which uniquely identifies each of the one or more vehicles and position data which indicates an initial position of each of the one or more vehicles when the one or more vehicles enter the field of view at the geographic location; a processor configured to determine current kinematic data of the one or more vehicles in at least two dimensions based upon the IR radiation detected by the one or more IR sensors, the received unique identification data and the received position data; and a transmitter configured to transmit the determined current kinematic data of a particular vehicle of the one or more vehicles to a kinematic data receiver spaced apart from the transmitter. The transmitter of a first vehicle tracking device is configured to transmit the current kinematic data determined at the first vehicle tracking device and unique identification data of the one or more vehicles to a second vehicle tracking device of the plurality of tracking devices and the receiver of the first vehicle tracking device is configured to receive current kinematic data determined at a third vehicle tracking device of the plurality of vehicle tracking devices and unique identification data of the one or more vehicles from a third vehicle tracking device.

Aspects an autonomous driving system with air support are described herein. The aspects may include an unmanned aerial vehicle (UAV) in the air and a land vehicle on the ground communicatively connected to the UAV. The UAV may include at least one UAV camera configured to collect first ground traffic information and a UAV communication module configured to transmit the collected first ground traffic information. The land vehicle may include one or more vehicle sensors configured to collect second ground traffic information surrounding the land vehicle, a land communication module configured to receive the first ground traffic information from the UAV, and a processor configured to combine the first ground traffic information and the second ground traffic information to generate a world model.

Systems and methods are provided to implementing a set of instructions related to a vehicle configuration template at a vehicle. Vehicle configuration templates may define instructions for automated vehicle driving parameters within a particular road region. The set of instructions within the first road region may correspond with a vehicle transition sequence based on the vehicle configuration template, where the vehicle transition sequence adjusts at least one of a vehicle position and dynamics to achieve a desired traffic flow.

A movement analytics platform can generate instructions for collecting video data from a site that includes a roadway. The instructions can be provided to a device having augmented reality capabilities, wherein the instructions include content displayed by the device to indicate an area at the site that an operator of the device is to position within a field of view of a camera. A data feed received from the device can include video data corresponding to the area and contextual data to annotate the video data based on observations by the device operator. The data feed can be processed to derive movement analytics associated with the area at the site (e.g., classifications, locations, speeds, travel directions, and/or the like for one or more objects depicted in the video data). The device can be provided with additional augmented reality content based on the movement analytics.

Provided are a navigation path planning method and apparatus, a device, and a storage medium. The navigation path planning method includes planning at least two available navigation paths for each target user of at least two target users in a target region; and determining a global passing feature of the target region and selecting, according to the global passing feature of the target region, one available navigation path from the at least two available navigation paths corresponding to each target user to serve as a recommended navigation path to be recommended to each target user.