A method for pushing multimedia information, an electronic device, and a non-transitory computer readable storage medium are provided. The method includes the following. Upon detecting that a vehicle is in a traveling state and the vehicle is traveling at a speed lower than a preset speed threshold within a preset time period, navigation data of the vehicle is provided. Duration that the vehicle is in a congestion state is estimated according to the navigation data. Collect scene information in the vehicle. Push multimedia information according to the duration that the vehicle is in the congestion state and the scene information in the vehicle.

A method for pushing multimedia information, an electronic device, and a non-transitory computer readable storage medium are provided. The method includes the following. Upon detecting that a vehicle is in a traveling state and the vehicle is traveling at a speed lower than a preset speed threshold within a preset time period, navigation data of the vehicle is provided. Duration that the vehicle is in a congestion state is estimated according to the navigation data. Collect scene information in the vehicle. Push multimedia information according to the duration that the vehicle is in the congestion state and the scene information in the vehicle.

The disclosure generally relates to autonomous or semi-autonomous driving vehicles. An exemplary embodiment of the disclosure relates to a system to provide one or more threat vectors to a cluster of vehicles. An exemplary vehicle detection system includes a communication module configured to receive a first threat vector from a first vehicle in a cluster of vehicles. The first threat vector may include a plurality of primary attributes associated with a moving object. The vehicle detection system may also include a detector module configured to detect the moving object and to provide one or more secondary attributes associated with the moving object; and a controller to construct a second threat vector as a function of one or more of the first threat vector, the primary attributes and the secondary attributes associated with the moving object.

A system, a method, and a computer program product may be provided for generating traffic congestion estimation data of one or more lanes in a region. A system may include a memory configured to store computer program code and a processor configured to execute the computer program code to obtain image data associated with the region. The processor may be configured to determine a count of one or more first movable objects in one or lanes, based on image data, calculate a lane object static capacity of the one or more lanes, based on one or more map free flow or speed limit attributes associated with the one or more lanes and generate the traffic congestion estimation data based on count of first movable objects in the one or more lanes, the moving speed of movable objects crossing multiple image frames, the lane object static capacity of lanes.

A system, a method, and a computer program product may be provided for generating traffic congestion estimation data of one or more lanes in a region. A system may include a memory configured to store computer program code and a processor configured to execute the computer program code to obtain image data associated with the region. The processor may be configured to determine a count of one or more first movable objects in one or lanes, based on image data, calculate a lane object static capacity of the one or more lanes, based on one or more map free flow or speed limit attributes associated with the one or more lanes and generate the traffic congestion estimation data based on count of first movable objects in the one or more lanes, the moving speed of movable objects crossing multiple image frames, the lane object static capacity of lanes.

A control system and a method for vehicle control in geographical control zones is provided. The control system receives traffic information, including a plurality of image frames of a group of moving objects in a geographical control zone and generates a set of images frames of a first moving object of the group of moving objects based on application of a trained Neural Network (NN) model on the received traffic information. The generated set of image frames corresponds to a set of likely positions of the first moving object at a future time instant. The control system predicts the unsafe behavior of the first moving object based on the generated set of image frames and generates first control information, including an alternate route for a first vehicle in the geographical control zone based on the predicted unsafe behavior. The first vehicle is controlled based on the generated first control information.

A control system and a method for vehicle control in geographical control zones is provided. The control system receives traffic information, including a plurality of image frames of a group of moving objects in a geographical control zone and generates a set of images frames of a first moving object of the group of moving objects based on application of a trained Neural Network (NN) model on the received traffic information. The generated set of image frames corresponds to a set of likely positions of the first moving object at a future time instant. The control system predicts the unsafe behavior of the first moving object based on the generated set of image frames and generates first control information, including an alternate route for a first vehicle in the geographical control zone based on the predicted unsafe behavior. The first vehicle is controlled based on the generated first control information.

Disclosed is a method for short-term traffic risk prediction of road sections by using roadside observation data. The method includes the following steps: 1) vehicle trajectory data in the detection area is obtained by using roadside observation data; 2) according to the continuous driving trajectories in the detection area, the traffic flow indicators are counted, and the surrogate safety indicators between vehicles are calculated; 3) time to collision and deceleration are selected as identification indicators to identify conflict events with collision risk in the detection area; 4) traffic flow indicators and surrogate safety indicators within the set time before the occurrence of conflict events are extracted, and the feature screening of various extracted indicators is performed by using classification algorithms; 5) based on the selected feature indicators, the indicators with the highest importance ranking are selected as the input to build a short-term traffic risk prediction model, and the model training and testing are completed by using the identified conflict events; 6) the short-term traffic risk prediction model is used to predict the risk of road sections. The proposed method can improve the prediction accuracy rate of road sections.

Disclosed is a method for short-term traffic risk prediction of road sections by using roadside observation data. The method includes the following steps: 1) vehicle trajectory data in the detection area is obtained by using roadside observation data; 2) according to the continuous driving trajectories in the detection area, the traffic flow indicators are counted, and the surrogate safety indicators between vehicles are calculated; 3) time to collision and deceleration are selected as identification indicators to identify conflict events with collision risk in the detection area; 4) traffic flow indicators and surrogate safety indicators within the set time before the occurrence of conflict events are extracted, and the feature screening of various extracted indicators is performed by using classification algorithms; 5) based on the selected feature indicators, the indicators with the highest importance ranking are selected as the input to build a short-term traffic risk prediction model, and the model training and testing are completed by using the identified conflict events; 6) the short-term traffic risk prediction model is used to predict the risk of road sections. The proposed method can improve the prediction accuracy rate of road sections.

A method of operating a vehicle having a driver assistance system includes detecting driving parameters pertaining to the vehicle while the vehicle is being driven on a roadway using a sensor system of the vehicle. Objects including road signs, lane indicators, and other vehicles are detected using the sensor system. The objects include at least road signs, lane indicators, and other vehicles on the roadway. A traffic rule pertaining to the roadway is identified using a traffic violation warning and prevention system of the driver assistance system. A traffic situation pertaining to the traffic rule is detected based on the detected objects and the driving parameters. An alert is generated that warns the driver of a potential traffic violation when the traffic situation is detected. Alternatively, the driver assistance system may be configured to take control of the vehicle to prevent violation of the traffic rule.