Hybrid Vision Perception for Low-Cost Autonomous Vehicle Navigation

High-performance perception systems for autonomous vehicles (AVs) commonly use LiDAR, RADAR, and multiple cameras. These sensors provide useful information, but their cost and computational requirements can be difficult to justify for small prototypes and low-cost platforms. This work therefore uses a monocular camera and combines three vision methods: Canny edge detection for boundary information, ArUco markers for localization, and YOLOv4 for object detection. The outputs are used to generate navigation commands on a commercially available four-wheel electric vehicle platform equipped with the prototype control system. The experiments compare the individual image-processing methods and evaluate the complete system under different visual conditions. The results indicate that the combined approach can provide useful localization and obstacle detection without requiring a large sensor suite.