Algorithm 1: Hybrid decision-making for autonomous vehicle navigation

Data: Camera frames $F$, ArUco dictionary, edge threshold $T_{edges}$, YOLOv4 detector

Result: Vehicle movement command

Initialize: camera, ArUco settings, YOLOv4 detector;

while destination not reached do

Capture frame $F$; convert to grayscale $G$;

Apply Canny edge detection → edges $E$; dilate $E$;

Count edge pixels: $N_{edges}$;

Detect ArUco markers in $G$ → IDs, dimensions;

Detect objects in $F$ using YOLOv4 → class labels;

MarkerValid ← False; ObstacleDetected ← False;

foreach marker do

if $w_{marker}\ge 17$ and $h_{marker}\ge 19$ then

MarkerValid ← True;

end

end

foreach YOLO detection do

if class ∈ {person, car, bicycle, motorbike} then

ObstacleDetected ← True;

end

end

if ObstacleDetected then

Command ← STOP

else if MarkerValid && $N_{edges}>T_{edges}$ then

Command ← TURN RIGHT

else if MarkerValid && $N_{edges}\le T_{edges}$ then

Command ← MOVE FORWARD

else

Command ← STOP

end

Display $F$, $E$, detections, and decision;

if user presses ‘q’ then

break;

end

end