System detects vehicle density at traffic junctions and allots the required time to traffic lights for vehicle passage dynamically. Classifier is trained using positive and negative images. Further, Adaptive Boosting is used to combine a number of weak classifiers into a strong one. The outcome of AdaBoost is Trained Cascade, which is finally used to detect vehicles in the images clicked.The system uses webcam to detect vehicles, count them and allow traffic passage according to traffic density of each lanes.
The interface of the autonomous car with the surroundings must be similar to that of human way of interaction. Humans use their eyes as a source of vision and then processes the visual signals in his/her brain and takes the necessary action accordingly. Similarly the autonomous car uses a camera as a visual source to know its surrounding, path etc. and uses the image processing techniques on the images received from the camera. This processing takes place on a minicomputer (RASPBERRY PI). After image processing, control instructions are passed on to the driving motors which helps in steering the vehicle accordingly.