US Department of Transportation (DOT) operators commonly use adjustable surveillance cameras for traffic monitoring and desire to have an automated traffic counting system by lane. To fill this need, this paper describes an automatic, novel, multiple-ROI (Regions of Interest) lane learning (MRLL) system. It detects lane centers, boundaries, and traffic directions, irrespective of zoom or direction. It finds optimal ROIs without user input by analyzing confidence scores from a chosen Machine Learning (ML) object detector. A simple but effective Continual Learning strategy is used to control the MRLL’s start and stop that optimizes lane counting performance in various real-world conditions: nighttime, extremely harsh weather, or low traffic flow conditions. Tested on 45 varied videos, it achieves an F1_score above 0.79 for lane center detection, 0.88 for lane boundaries, and 94% accuracy in traffic direction detection. This innovative system, which does not rely on lane markings and adapts to camera views, is currently used by the Indiana Department of Transportation for vehicle counting and flow rate estimation in real-world ITS scenarios. Code is available at https://github.com/qiumei1101/Multiple_ROI_lane_learning_system_for_Highway.git.