AUTONOMOUS VEHICLE TRAFFIC RECOGNITION BASED ON ARTIFICIAL INTELLIGENCE

Abstract

Self-driving vehicles, or self-driving cars, can navigate themselves without a driver because they are aware of their surroundings. Although it is one of the most intelligent features, lane-keeping assist sometimes fails in practical situations. In order to prevent this, the camera is firmly mounted on the hood. The video is then preprocessed and thus providing the masked area of the road to apply the Hough transform technique to detect traffic in the current lane of the motorway. An intelligent transportation system relies heavily on deeply integrated smart cars. The steering wheel is steered towards the designated lane using Arduino UNO. In spite of the great potential for vision-based autonomous driving, it remains difficult to assess complex traffic scenarios based on the data collected. In recent years, autonomous driving has been divided into a variety of individual tasks through the use of different models, such as object detection and intent recognition. This study presents a vision-based system for recognizing various objects in the traffic context, identifying them, and predicting pedestrian intentions. With this technique, most accidents can be prevented, and the trust of people is also increased.