Real-time detection of traffic situation

A system comprising:

• one or more processors;
• one or more memories storing instructions that, when executed by the one or more processors, cause the system to:
• extract a region of interest (ROI) from an image depicting a roadway;
• execute a first object detector that detects, in the ROI, a first situation object matching a first object type based on object features of the first situation object, the first situation object reflecting a first physical roadway object depicted in the ROI;
• generate a first object confidence score for the first situation object based on a level of similarity between the first situation object and the first object type;
• execute, in parallel with the first object detector, a second object detector that detects, in the ROI, a second situation object matching a second object type that is different from the first object type based on object features of the second situation object, the second situation object reflecting a second physical roadway object depicted in the ROI;
• generate a second object confidence score for the second situation object based on a level of similarity between the second situation object and the second object type;
• determine that the first object type detected by the first object detector and the second object type detected by the second object detector are associated with a first prospective situation category;
• combine the first object confidence score and the second object confidence score into a first situation confidence score corresponding to the first prospective situation category;
• and
• evaluate the first situation confidence score to determine that the first prospective situation category applies to the ROI and that a first traffic situation defined by the first prospective situation category is depicted in the ROI.

A method comprising:

• extracting a first region of interest (ROI) from a first image depicting a roadway;
• executing, in parallel, a first object detector that detects a first situation object matching a first object type in the first ROI and a second object detector that detects a second situation object matching a second object type in the first ROI, the first object type being different from the second object type;
• generating a first object confidence score for the first situation object and a second object confidence score for the second situation object;
• determining that the first object type detected by the first object detector and the second object type detected by the second object detector are associated with a first prospective situation category;
• combining the first object confidence score and the second object confidence score into a first situation confidence score corresponding to the first prospective situation category;
• evaluating the first situation confidence score to determine that the first prospective situation category does not apply to the first ROI of the first image and that a first traffic situation defined by the first prospective situation category is not depicted in the first ROI of the first image;
• extracting a second ROI from a second image depicting the roadway;
• executing, in parallel, the first object detector that detects a third situation object matching the first object type in the second ROI and the second object detector that detects a fourth situation object matching the second object type in the second ROI;
• generating a third object confidence score for the third situation object and a fourth object confidence score for the fourth situation object;
• combining the third object confidence score and the fourth object confidence score into a second situation confidence score corresponding to the first prospective situation category;
• and
• evaluating the second situation confidence score to determine that the first prospective situation category applies to the second ROI of the second image and that the first traffic situation defined by the first prospective situation category is depicted in the second ROI of the second image.