A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.

A driver assistance system of an ego vehicle is operated. The ego vehicle has at least one surroundings sensor for detecting the surroundings of the ego vehicle. Movements of multiple vehicles are detected with the at least one surroundings sensor in the surroundings of the ego vehicle. A movement model is generated based on the detected movements of the respective vehicles. A traffic situation is ascertained and a probability of correct classification of the traffic situation on the basis of the generated movement model by a machine learning method. The traffic situation and the probability of the correct classification of the traffic situation are ascertained by the machine learning method on the basis of the learned characteristic features of the movement model. The driver assistance system of the ego vehicle is adapted to the ascertained traffic situation.

A positioning system and a calibration method of an objection location are provided. The calibration method includes the following. Roadside location information of a roadside unit (RSU) is obtained. Object location information of one or more objects is obtained. The object location information is based on a satellite positioning system. An image identification result of the object or the RSU is determined according to images of one or more image capturing devices. The object location information of the object is calibrated according to the roadside location information and the image identification result. Accordingly, the accuracy of the location estimation may be improved.

Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.

Systems and techniques are described for consuming data in an intelligent transport system. In some implementations, a system includes a display screen device and sensors. The sensors generates data describing sensor observations of a roadway at a first location and provides data describing the observations to the display screen device. The display screen device receives the data and determines an event and a type of the event. The display screen device displays second data indicative of the type of event, the second data being of a format that is consumable by a sensor on a vehicle traversing the roadway towards the first location, the sensor (i) located within a first resolution distance from the display screen device and (ii) located outside a second resolution distance of detecting the event, wherein the second data is used by an on-board processing system of the vehicle to adjust its driving behavior.

Automatic license plate recognition occurs when a light sensor that continually captures video detects motion as a vehicle is driven through a gate. The light sensor detects the vehicle and license plate in the video stream captured by the light sensor. An algorithm associated with the video stream of the light sensor is trained to detect license plates. The light sensor starts executing the recognition algorithm when it detects motion. Recognition of characters in the license plate is based upon an aggregation of several captured video frames in which a license plate is detected.

Automatic license plate recognition occurs when a light sensor that continually captures video detects motion as a vehicle is driven through a gate. The light sensor detects the vehicle and license plate in the video stream captured by the light sensor. An algorithm associated with the video stream of the light sensor is trained to detect license plates. The light sensor starts executing the recognition algorithm when it detects motion. Recognition of characters in the license plate is based upon an aggregation of several captured video frames in which a license plate is detected.

A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.

A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.

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