A method is for assisting with the driving of vehicles traveling on a road by an assistance system including a sensor installed along sections of the road and an electronic processor. The method includes reception by the processor of a request indicating a road portion and requesting information on the state of the portion, determination by the processor, as a function of the requested information and the road portion, of the configuration among several determined configurations, of the sensor, sending by the processor to the sensor of a command indicating the configuration, sending the processor data delivered by the sensor in the configuration, and sending by the processor of the information determined as a function of the data.

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.

A method for perception-sharing between similarly-situated vehicles that are traveling on a portion of a roadway that is equipped with an intelligent vehicle highway system is described, and includes executing, in a multi-access edge computing cluster in communication with a roadside unit disposed to monitor a roadway, an application-layer routine. The application-layer routine includes collecting real-time data associated with a plurality of objects from each of the similarly-situated vehicles, predicting motion of each of the plurality of objects based upon the real-time data, object-matching the motion of each of the plurality of objects, and executing fusion of the plurality of objects based upon the object-matching of the motion of each of the plurality of objects. Locations of the similarly-situated vehicles traveling on the roadway are identified based upon the fusion of the plurality of objects. The locations are communicated to one of the similarly-situated vehicles.

Systems, methods, and devices are disclosed for improving traffic safety and efficiency. The system includes various signal transmitters and receivers positioned throughout roadways, within automobiles, in smartphones, or supported by a cellular network backbone, for distributing traffic related information to users and traffic controller equipment. Embodiments of the present disclosure allow for vehicles and/or pedestrians to initiate a dual-transmission of cellular and RF signals for changing a traffic light state, where the first signal received at a traffic intersection controller unit is processed for changing the traffic light state (e.g., changing a light from red to green on-demand). Other embodiments of the present disclosure allow for users to receive visible and/or audible traffic related alerts on mobile devices, where the alerts are based on data shared between nearby drivers, pedestrians, and the traffic controlling equipment.

Presently disclosed is a system, apparatus, and method for navigating and orienting roadway vehicles by use of a network of embedded navigation beacons within a roadway. A plurality of primary navigation beacons are embedded into a roadway surface with sensors, and communicate with a car and a smaller subset of secondary beacons with connection to the internet. Further disclosed is a landing pad for a drone delivery system, the landing pad acting as a navigational beacon and safe landing location indicator for the aerial drone.

A method is described for performing a real time analysis of traffic flow control device related data using a plurality of vehicles connected in at least one vehicle cell network. The method compares position data from vehicles defining a traffic flow with received data for a traffic flow control device and geometry and topology data concerning a transport engineering construction related to the traffic flow control device to evaluate if a discrepancy can be detected between the geometry and topology data concerning the transport engineering construction and the traffic flow defined by the vehicles positions. The method may be used for updating and/or finding errors in the geometry and topology data concerning the transport engineering construction.

A system, a method and a device for planning a driving path for a vehicle are described. In one example aspect, the device is configured to: analyze sense data to obtain positioning data of vehicles; assign vehicle transportation tasks to an unmanned vehicle and a manned vehicle in the predetermined area in accordance with a predetermined transportation task, each vehicle transportation task including a transportation start point and a transportation end point; plan driving paths for the unmanned vehicle and the manned vehicle based on the assigned vehicle transportation tasks, the vehicle positioning data and map data; transmit the assigned transportation task and the planned driving path for the unmanned vehicle to the unmanned vehicle; and transmit the assigned transportation task and the planned driving path for the manned vehicle to a mobile device corresponding to the manned vehicle.

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.

The present invention relates to a portable traffic management system comprising a portable traffic signal assembly; a portable signal assembly; and a control unit in wireless communication with the portable traffic signal assembly and the portable signal assembly. The present invention alleviates one or more of the problems associated with the presently available traffic management systems.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.