A method for intersection assistance, the method may include obtaining sensed information regarding an environment of the vehicle; determining an occurrence of an intersection related situation, based on the sensed information; generating one or more intersection driving related decisions; wherein the generating comprises processing, by one or more narrow AI agents of a group of narrow AI agents, at least one out of (a) at least a first part of the sensed information, and (b) an outcome of a pre-processing of at least a second part of the sensed information; and responding to the one or more intersection driving related decisions; wherein the responding comprises at least one out of (a) executing the one or more intersection driving related decisions, and (b) suggesting executing the one or more intersection driving related decisions.

A computer vision processor of a camera generates hyperzooms for persons or vehicles from image frames captured by the camera. The hyperzooms include a first hyperzoom associated with the persons or vehicles. The computer vision processor tracks traffic patterns of the persons or vehicles while obviating network usage by the camera by predicting positions of the persons or vehicles using a Kalman Filter from the first hyperzoom. The persons or vehicles are detected in the second hyperzoom. The positions of the persons or vehicles are updated based on detecting the persons or vehicles in the second hyperzoom. The first hyperzoom is removed from the camera. Tracks of the persons or vehicles are generated based on the updated positions. The second hyperzoom is removed from the camera. Track metadata is generated from the tracks for storing in a key-value database located on a non-transitory computer-readable storage medium of the camera.

A vehicle assistance system for a vehicle is provided. A corresponding computer implemented method and computer program product are also provided.

Traffic-counting methods and apparatus are disclosed. The methods may include, in a view of traffic comprising moving objects, identifying first and second regions of interest (ROIs). The methods may also include obtaining first and second image data respectively representing the first and second ROIs. The methods may also include analyzing the first and second image data over time. The methods may further include, based on the analyses of the first and second image data, counting the moving objects and determining moving directions of the moving objects.

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.

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.

A record-and-replay control device includes a video data acquisition unit configured to acquire video data from an imaging unit to capture an image of outside of a vehicle; an event detector configured to detect an event of the vehicle; an event detection direction acquisition unit configured to acquire an event detection direction of the event; a recording controller configured to store video data caused by the detected event as event recording data with a first angle of view; a replay controller configured to replay selected event recording data; and a display controller configured to control a display unit to display, out of thumbnail images of the stored event recording data, a thumbnail image indicating the event recording data to which the event detection direction is correlated, as a thumbnail image obtained by adopting a second angle of view with enlargement in the event detection direction.

A system includes one or more video capture devices and a processor coupled to each video capture device. Each processor is operable to direct its respective video capture device to obtain an image of a monitored area and process the image to identify objects of interest represented in the image. The processor is also operable to generate bounding perimeter virtual objects for the identified objects of interest, each bounding perimeter virtual object surrounding at least part of its respective object of interest. The processor is further operable to determine danger zones for the identified objects of interest based on the bounding perimeter virtual objects. The processor is further operable to determine at least one near-miss condition based at least in part on an actual or predicted overlap of danger zones for multiple objects of interest, and may optionally generate an alert at least partially in response to the near-miss condition.