Autonomous Vehicle Navigation Using IoT Sensors and Deep Learning for Obstacle Detection

Autonomous driving greatly relies on the correct object detection and scene comprehension. Nevertheless, it has been a continuous challenge to make sure that there is real-time accuracy in dynamic and unpredictable environments. The classical approaches have drawbacks in terms of detecting performance enhancement, computational effectiveness, and responsiveness, which degrade the security and reliability of self-driven cars based on the Internet of Things (IoT). To overcome those issues, in this paper, I have introduced YOLOSwinNet, a hybrid framework that will combine YOLOv8 with the Swin Transformer to improve perception in autonomous navigation. The proposed model takes advantage of the rapidity and lightweight characteristics of YOLOv8 and hierarchical feature acquisition of the Swin Transformer and enhances the detection performance in complex urban environments. YOLOSwinNet was developed to be effective in processing information produced by the IoT in order to allow a smoother navigation process and ensure successful obstacle avoidance. It is unique in that it integrates a convolutional architecture and transformer-based architecture to attain a context-aware perception with enhanced spatial awareness and flexibility to various road conditions. A superior attention process is also used to refine the outputs of the detection process by minimizing false positives and enhancing decision-making. Experimental results show superior performance, achieving mAP@50 of 0.98348 and mAP@50–95 of 0.9870. Comparative evaluations demonstrate YOLOSwinNet’s advantages over state-of-the-art models in precision, recall, and inference speed, offering scalability, interpretability, and energy efficiency for next-generation autonomous vehicle systems.