A vehicle navigation system includes a camera and a controller. The camera is configured to render an image of a host-vehicle in a field-of-view of the camera. The camera located remote from the host-vehicle. The controller is installed on the host-vehicle. The controller is configured to receive the image and determine a vehicle-coordinate of the host-vehicle in accordance with a position of the host-vehicle in the image. The camera may be configured to superimpose gridlines on the image, and the controller may be configured to determine the position in accordance with the gridlines.

Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

An automated vehicle control distributed network node, that includes at least two modems for communicating with two neighboring roadside nodes on the same side of the roadway; at least one antenna for communicating with vehicles via a wireless connection; pattern recognition processing operative to detect patterns using image data from a plurality of high speed, high resolution video cameras that include night vision; vehicle prediction processing, operatively coupled to the pattern recognition processing, operative to predict vehicle location, velocity and direction using the pattern recognition processing; and a vehicle controller, operatively coupled to the vehicle prediction processing to receive vehicle prediction data, and to the at least one antenna, operative to send acceleration, deceleration and steering control signals to a plurality of vehicles in response to vehicle prediction data received from the vehicle prediction processing.

A controlling and warning system based on traffic conditions feedback and a method thereof are disclosed. In the controlling and warning system, a camera disposed on a rear of a vehicle body is configured to generate and transmit a rear video to a controlling host, and the controlling host identifies a vehicle object in the rear video and calculates a separation distance between the vehicle object and the vehicle body; the vehicle object is inputted into a large-sized vehicle recognition model which is built based on artificial intelligence neural network and trained completely, to recognize whether the vehicle object is a large-sized vehicle; when the vehicle object is recognized as the large-sized vehicle and the separation distance reaches to a safe distance, a warning signal is generated. Therefore, the technical effect of improving the warning immediacy of an approaching large vehicle can be achieved.

There is provided a signal processing apparatus, a signal processing method for appropriate driving control. The signal processing apparatus includes a safety determination unit that determines safety of a preceding vehicle on a basis of sensor-related information related to a sensor provided in the preceding vehicle, the sensor being relevant to a safety function, and a control signal generation unit that generates, on a basis of a result of the determination of the safety, a control signal for controlling a host vehicle.

A peccancy monitoring system and a peccancy monitoring method are provided in the present application. The peccancy monitoring system includes: a first camera, configured to monitor whether a peccancy vehicle is present in a designated area and generate alarm message when the peccancy vehicle is present; a control unit, connected to the first camera and configured to trigger the second camera to shoot according to the alarm message; a second camera, connected to the control unit and configured to shoot the peccancy vehicle to acquire a video of the vehicle and an image of the vehicle, here the video of the vehicle is a video containing a license plate of the peccancy vehicle and the image of the vehicle is an image containing the license plate of the peccancy vehicle. By using the technical solutions in the present application, the road can be monitored after the school bus is docked.

The present disclosure relates to an information processing device and method, and a program that enable processing corresponding to a recognition result with respect to a license plate of a vehicle.

On the basis of a recognition result with respect to a license plate of a vehicle in a captured image, characteristic information of the vehicle is extracted from the captured image, and processing corresponding to the extracted characteristic information of the vehicle is performed. The present disclosure can be applied to, for example, an information processing device, an image processing device, a communication device, an electronic apparatus, an information processing method, a program, or the like.

A system is provided and generally includes a server and an associate computing device. The server may receive location video data comprising at least one image from a camera directed to a location, such as a designated area of a parking lot. The server may detect one or more objects in the image, and determine a bounding box based on the detected objects. The server may also determine a confidence value corresponding to the bounding box. The confidence value may be determined based on an area of the image defined by the bounding box and a predefined class. The server may then generate an arrival message based on the determined confidence value, and transmit the arrival message to the associate computing device.

This disclosure discloses a vehicle scheduling method, apparatus and system, and relates to the field of scheduling. The method includes acquiring image information of each area; determining vehicle density information in each area according to the image information; configuring a first path cost corresponding to the path of each area according to the vehicle density information in each area; calculating a path value corresponding to each planning path among a plurality of planning paths from the starting position to at least one target position for a target vehicle, according to the first path cost configured for the path of each area; and determining an optimal planning path of the target vehicle by taking a minimum path value as a target.

The present method permits to get real-time traffic data by the mean of pictures took by a series of georeferenced and synchronized high speed cameras installed on the portions of the road. These pictures and these data will be transferable by a secure means such as 5G or any other fast and secure technology on a server and accessible by cloud computing. Picture processing is carried out by photogrammetric, triangulation and picture recognition approaches in order to extract the position of each vehicle, pedestrian, cyclist or any object and identify its x, y, z coordinates in a local or global referencing system. This method permits to count the flow of traffic (vehicles, pedestrians, etc.) passing through these roads portions, to reproduce the real movements of vehicles, pedestrians and any object moving on a road, make simulations with a computer and intervene remotely in real-time to manage traffic.