A method (and system, apparatus, and computer product) for reporting traffic data includes, in an onboard unit installed in a vehicle, generating a traffic data report packet including information identifying a location of the vehicle in a traffic network and storing the generated traffic data report packet in a transmission queue. The onboard unit stores and maintains the generated traffic data report packet in the transmission queue with other traffic data report packets in a sequence in accordance with potential transmission times associated with each traffic data report packet. At least a portion of the traffic data report packets stored in the transmission queue is selectively transmitted, as a batch transmission to a traffic center server via a communication link.

A traffic control system includes an identification unit (234) that identifies a vehicle falling under a predetermined condition that has been set, a position control unit (235) that sets a relative position between the vehicle identified and a surrounding vehicle around the vehicle based on a relative distance between the vehicle and the surrounding vehicle, and a control unit (230) that generates control information for controlling movement of the vehicle according to the set relative position.

In identifying vehicle navigation requirements using augmented reality, one vehicle determines that an event has occurred that concerns operation of the vehicle. The vehicle determines its current location, creates a notification message corresponding to navigation requirements matching the current location and the event, and sends the notification message. Another vehicle receives the notification message. In response, the other vehicle determines its current environment and a correlation between the navigation requirements and the current environment. An augmented reality system of the other vehicle displays alerts for the navigation requirements overlaid on a display of the current environment based on the correlation. I this manner, vehicle drivers are alerted to the navigation requirements applicable in particular locations or jurisdictions.

Systems, methods, and computer-readable media are described for performing real-time vehicular incident risk prediction using real-time vehicle-to-everything (V2X) data. A vehicular incident risk prediction machine learning model is trained using historical V2X data such as historical incident data and historical vehicle operator driving pattern behavior data as well as third-party data such as environmental condition data and infrastructure condition data. The trained machine learning model is then used to predict the risk of an incident for a vehicle on a roadway segment based on real-time V2X data relating to the roadway segment and/or vehicle operators on the roadway segment. A notification of a high risk of incident can then be sent to a V2X communication device of the vehicle to inform an operator of the vehicle.

A traffic data analysis method, an electronic device, a vehicle and a storage medium are provided, and relate to the technical field of artificial intelligence, in particular to the fields of large data processing, automatic driving and vehicle networking, and can be applied to AI navigation. The method includes: acquiring a plurality of initial traffic data; determining a category of each of the plurality of initial traffic data; receiving a search instruction from an AI analysis model, wherein the search instruction includes target category information; determining target traffic data corresponding to the target category information from the respective initial traffic data according to categories of the respective initial traffic data; and sending the target traffic data to the AI analysis model so that the AI analysis model performs an AI analysis according to the target traffic data.

One variation of a method for accessing supplemental data from other vehicles includes, at an autonomous vehicle: recording a scan image of a scene around the autonomous vehicle at a first time; detecting insufficient perception data in a region of the scan image; in response to detecting insufficient perception data in the region, defining a ground area of interest containing the region and wirelessly broadcasting a query for perception data representing objects within the ground area of interest; in response to receiving supplemental perception data—representing objects within the ground area of interest detected by the second vehicle at approximately the first time—from a second vehicle proximal the scene, incorporating the supplemental perception data into the scan image to form a composite scan image; selecting a navigational action based on objects in the scene represented by the composite scan image; and autonomously executing the navigational action.

A computer-implemented method and system for usage-controlled service delivery to an onboard system of a vehicle are designed for delivering updatable services to the onboard system of the vehicle by way of a vehicle-external server according to data of the service usage behavior of a vehicle user of the updatable services.

An apparatus comprising an infotainment system including a proactive automotive assistant that executes a first action and a second action, wherein the first action is that of permitting spontaneous communication to an occupant in a vehicle and the second action is that of providing information indicating that spontaneous communication with the occupant is impermissible. The automotive assistant is configured to receive information selected from the group consisting of vehicle-status information concerning operation of the vehicle and occupant-status information concerning the occupant and to base the first and second actions at least in part on the information.

A navigation system includes a server apparatus connected to vehicles via a network and a navigation apparatus connected to the server apparatus via the network. The server apparatus receives, from a first vehicle, traffic-related information related to snow on a road on which the first vehicle has traveled or slipperiness of the road, and location information of the road; generates difficulty information for the road related to a travel difficulty caused by the snow or the slipperiness; and transmits the difficulty information and the location information to the navigation apparatus movable together with a second vehicle identical to or different from the first vehicle. The navigation apparatus receives the difficulty information and the location information; sets a traffic regulation for passage of the second vehicle through the road; and sets, for the second vehicle, a travel route based on the traffic regulation.

A hazard prediction and a hazard avoidance procedure are performed based on a predetermined hazard avoidance condition during an autonomous driving operation of a vehicle. User-input information about a driving operation of the vehicle; is received, and area or object information is checked to determine the area or the object as a monitoring target. The monitoring target is tracked. At least one additional hazard avoidance procedure for the monitoring target is set, and the at least one additional hazard avoidance procedure is performed in addition to the hazard avoidance procedure when the monitoring target approaches the vehicle.