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.

A method for preventing an operation of a car application that causes a quality of service of an operation of a computer system of a vehicle to be reduced below a threshold can include receiving, from a separate source, a value indicative of the quality of service of the operation of the computer system during the operation of the car application in conjunction with the operation of the computer system. The method can include determining an existence of a condition. The condition can be that the value is less than the threshold. The method can include causing, in response to a determination of a lack of the condition, the car application to be in a condition to be operated on the vehicle. The method can include preventing, in response to a determination of the existence of the condition, the car application from being in the condition to be operated.

Disclosed are systems and methods relating to providing corridor metrics based on road network data and telematic data.

Disclosed are systems and methods relating to providing corridor metrics based on road network data and telematic data.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

Location technologies are combined with other information systems to provide augmented information for individuals such as a traveler in an automobile.

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.

An example operation may include one or more of identifying transports operating on a roadway, determining a degree of transport traffic based on the identified transports, when the degree of transport traffic has exceeded a traffic level, assigning one or more of the identified transports as a roadway manager, controlling movement of the roadway manager, and generating a roadway indicator on the roadway manager to direct the identified transports.

Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.