A method for predicting a remaining time of a signal phase includes capturing traffic data and a signal phase specification distinguishing different signal phases of a traffic signal generator. The traffic data is fed as input data to an artificial neural network including first and second sub-networks and a combination network for combining output data of the two sub-networks. The artificial neural network is trained to reproduce a time still remaining until a phase change of the traffic signal generator based on the traffic data. Outputting of the output data of the first and second sub-networks is controlled in a manner complementary to one another according to the signal phase specification. Lastly, the output data of the combination network or the prediction data derived therefrom are transmitted to a transport device or to a road user as a prediction of the time remaining for influencing traffic.

Driving behavior for a particular driver may be gathered and analyzed over a time period, such as a year or a duration of the driver's employment at a particular employer. The driving behavior is received by a server from a positioning device as multiple streams of position data at different time stretches throughout the time period, each stream of position data associated with a route of a plurality of routes driven by at least one vehicle. The server generates speed data from the streams of position data and compares the speed data to retrieved speed limit data for those routes. The server generates a report with at least a speeding percentage value corresponding to how often the driver was speeding while driving during the time period. The report is then sent to the driver's computing device.

Traffic signals that adapt to traffic conditions are provided with countdown timers. These countdown timers count down from some number towards zero, and indicate the approximate duration remaining before a traffic signal changes state. Since the traffic signal is continuously adapting to traffic conditions, the exact time before a state change occurs is not known in advance. Using a countdown algorithm, the countdown timers imperceptibly modify the countdown sequence in real time so that the traffic signal state change coincides approximately with the moment the countdown reaches its minimum count.

Traffic signals that adapt to traffic conditions are provided with countdown timers. These countdown timers count down from some number towards zero, and indicate the approximate duration remaining before a traffic signal changes state. Since the traffic signal is continuously adapting to traffic conditions, the exact time before a state change occurs is not known in advance. Using a countdown algorithm, the countdown timers imperceptibly modify the countdown sequence in real time so that the traffic signal state change coincides approximately with the moment the countdown reaches its minimum count.

A vehicle control device includes an external situation recognition unit configured to recognize a crossing person who crosses over a path of a vehicle and acquire information on the crossing person and information on an environment where the crossing person crosses, a scheduled departure time deciding unit configured to decide a scheduled departure time of the vehicle based on the information on the crossing person and the environment where the crossing person crosses when the crossing person is recognized by the external situation recognition unit, and an informing controller configured to perform a control to inform an outside of the vehicle of the scheduled departure time. The scheduled departure time deciding unit predicts a crossing completion time at which the crossing person recognized by the external situation recognition unit completes the crossing and decides the scheduled departure time based on the crossing completion time.

A system and a method manage wireless traffic signals in order to aid autonomous driving systems and humans to quickly and accurately detect traffic signal changes and surrounding traffic. The system includes a plurality of road vehicles. Each road vehicle includes a vehicle receiver, a vehicle controller, and a vehicle output device. The system also includes a plurality of traffic-signal sources and a plurality of signal emitters. Each signal emitter is associated to a corresponding traffic-signal source from the plurality of traffic-signal sources. Each signal emitter processes traffic signals from the corresponding traffic-signal source and sends the processed traffic signals to the vehicle receivers. The vehicle receivers receive signals from the plurality of signal emitters. The vehicle controller processes the signals received by the vehicle receiver. The vehicle output device executes instructions that are extracted from the signals processed by the vehicle controller.

A control device for a vehicle includes: a signal information acquisition unit that acquires signal information about at least one traffic light, from a server having signal information about a plurality of traffic lights provided at a plurality of intersections; and a vehicle control unit that controls the vehicle, based on the signal information acquired by the signal information acquisition unit.

Methods and systems for navigating a vehicle. The system includes an input/output device of the vehicle configured to receive a destination from a user of the vehicle. The system also includes a transceiver of the vehicle configured to receive traffic data and traffic light timing data of traffic lights between a current location of the vehicle and the destination. The system also includes an electronic control unit (ECU) of the vehicle configured to determine one or more routes from the current location to the destination based on the traffic data and the traffic light timing data.

A smart traffic control device implements a method for detecting faulty sensors that result in false green requests and/or undetected true green requests. The faulty sensors may include inductive loops and pedestrian pushbuttons. The traffic control device may communicate with remote resources, including computer systems connected to human resources, to help identify the faulty sensors. The human resources may be crowdsourced. The traffic control device's communications with the remote resources may go through vehicle computers and smart phones of vehicles' occupants and pedestrians. The traffic control device may emit signals to enable vehicle computers to improve accuracy of position location. The traffic control device may emit its state (e.g., Green/Yellow/Red in various directions) and time to state changes, in real or substantially real time. A vehicle computer may receive the state and/or time to state changes and vary operation of the vehicle's power train in response thereto.

A smart traffic control device implements a method for detecting faulty sensors that result in false green requests and/or undetected true green requests. The faulty sensors may include inductive loops and pedestrian pushbuttons. The traffic control device may communicate with remote resources, including computer systems connected to human resources, to help identify the faulty sensors. The human resources may be crowdsourced. The traffic control device's communications with the remote resources may go through vehicle computers and smart phones of vehicles' occupants and pedestrians. The traffic control device may emit signals to enable vehicle computers to improve accuracy of position location. The traffic control device may emit its state (e.g., Green/Yellow/Red in various directions) and time to state changes, in real or substantially real time. A vehicle computer may receive the state and/or time to state changes and vary operation of the vehicle's power train in response thereto.