A system (and method) includes at least one traffic server to receive traffic information reports transmitted from vehicles located on a traffic network serviced by the at least one traffic server. The traffic network is segmented into a plurality of predefined links, each predefined link associated with a priority level, a reporting latency, and an identification code. An onboard unit in a vehicle transmits traffic information reports to the at least one traffic server. The onboard unit is configured: to determine a current location of the vehicle; based on the current location, to determine on which of a link of the predefined links in the traffic network the vehicle is currently located; to determine a link identification code that corresponds to the link on which the vehicle is currently located; and to generate a traffic data packet to be potentially transmitted by the onboard unit to the at least one traffic server. The determined link identification code serves as indicia of location in the traffic data packet.

A method for filtering infrastructure description messages packed in data packages, the messages being transmitted in a vehicular ad hoc network, along with positional information messages for the localization of individual participating nodes, in order to describe the status of the vehicular ad hoc network and/or a street on which the participating nodes are located. The method includes the following steps: receiving one of the infrastructure description messages at a participating node; evaluating the received infrastructure description message as to whether a response is required; and filtering the evaluated infrastructure description message based on a predetermined criterion for whether a response is required.

An information providing apparatus includes a traffic information database and a data processing device. The data processing device detects, based on the traffic information data, an impediment event. The data processing device extracts the impediment event potentially encountered by an assisted vehicle. The data processing device calculates, based on the traffic information data, a certainty level of the impediment event. The data processing device provides a driver of the assisted vehicle with a straightforward notification regarding the impediment event potentially encountered by the assisted vehicle and having the certainty level equal to or higher than a straightforward notification threshold. The data processing device provides the driver with an euphemistic notification, which is different from the straightforward notification, regarding the impediment event potentially encountered by the assisted vehicle and having the certainty level less than the straightforward notification threshold.

A communication apparatus (10) includes: a communication unit (11) configured to transmit sensor information detected using at least two or more sensors, the two or more sensors being adapted to detect states of a mobile body in directions different from one another with the mobile body being a base point; a moving state recognition unit (12) configured to recognize a moving state; a peripheral state recognition unit (13) configured to recognize a peripheral state of the mobile body; and a communication control unit (14) configured to determine a priority level of each of the two or more sensors based on the moving state and the peripheral state and perform communication control so that the sensor information detected by the sensor having a high priority level is transmittable in quality higher than that of the sensor information detected by the sensor having a low priority level.

Methods and apparatus consistent with the present disclosure may receive different sets of image or sensor data that may be compared for inconsistencies. Each of these sets of received data may include images of a roadway or intersection that are processed to identify objects and object locations at or near the roadway/intersection. In certain instances data received from a camera at a traffic control system may be compared with data received from a computer at a vehicle. Alternatively or additionally, different sets of data acquired by a same or by different cameras may be compared for discrepancies. When one or more discrepancies are identified in different sets of received data, corrective actions may be initiated. In certain instances, such corrective actions may include recalibrating an image acquisition system or may include sending message to a vehicle computer that identifies the discrepancy.

A computing device includes a camera configured to capture images of an area of a road, the area defining a geofence; and a processor, configured to responsive to detecting a traffic density within the geofence exceeding a predefined threshold, wirelessly broadcast a directional message within the geofence to request vehicles located within the geofence to temporarily disable individual messaging services having low priorities identified in the directional message, analyze vehicle traffic using images captured by the camera to detect a predefined traffic situation, responsive to detecting the predefined traffic situation initiated by one of the vehicles, generate a safety message reflecting the traffic situation, and broadcast the safety message to vehicles within the geofence.

A method and a system are disclosed for predicting that a road object is in a road zone or not. The method may include receiving at least one road object observation associated with the road object; extracting at least one feature associated with the road object or surroundings thereof based on the received at least one road object observation; and predicting, using a trained machine learning model, that the road object is in the road zone or not based on the extracted at least one feature, wherein the machine learning model is trained based on a training data set comprising a combination of at least one training feature and a ground truth label data, wherein the ground truth label data comprises at least one of a road zone data and a non-road zone data.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

Aspects of the present disclosure include methods, apparatuses, and computer readable media for receiving a plurality of requests, from a plurality of user equipments (UEs), to exit a parking area comprising a plurality of vehicles, wherein each of the plurality of UEs is associated with a corresponding vehicle of the plurality of vehicles, determining an exit order for the plurality of vehicles to exit the parking area, and transmitting, to the plurality of UEs, a plurality of exit commands, based on the exit order, for the plurality of vehicles to exit the parking area.

A tool for inter-vehicle communication. The tool determines whether contextual data and journey information for a user justifies a notification. Responsive to a determination that the contextual data and the journey information justifies the notification, the tool generates the notification. The tool generates a trust score for the notification. The tool determines one or more vehicles located within a pre-defined proximity of the user. The tool transmits the notification and the trust score to the one or more vehicles located within the pre-defined proximity of the user.