A Hybrid AI Framework for Autonomous Driving Across Diverse Traffic and Weather Conditions

Abstract

Autonomous driving systems must operate safely and reliably under diverse traffic densities, varying weather conditions, and dynamic road environments while maintaining real-time performance and low computational cost. However, many existing autonomous driving frameworks rely on expensive LiDAR sensors, high-performance computing hardware, and cloud-based processing, limiting their practical deployment in cost-sensitive applications. This paper proposes a Hybrid AI Framework for Autonomous Driving Across Diverse Traffic and Weather Conditions that integrates Segment Anything Model 2 (SAM2) for semantic scene segmentation, transformer-based multimodal sensor fusion for comprehensive environmental perception, reinforcement learning (RL) for adaptive decision-making, and a neural vehicle controller for continuous steering, throttle, and brake control. To reduce system cost and computational complexity, the framework employs a low-cost sensor suite comprising an RGB camera, automotive radar, ultrasonic sensors, GPS, accelerometer, gyroscope, odometer, magnetometer, temperature sensor, humidity sensor, and vibration sensor, eliminating the need for expensive LiDAR systems. The entire architecture is optimized for deployment on the low-cost NVIDIA Jetson embedded edge-computing platform, enabling real-time processing with reduced latency, lower power consumption, and improved operational efficiency. The proposed framework is designed to handle challenging edge-case scenarios, including sudden obstacles, adverse weather, dense traffic, and low-visibility conditions, by dynamically adapting sensor fusion and driving policies. Experimental evaluation demonstrates that the proposed approach achieves over 90% perception and decisionmaking accuracy, while maintaining stable vehicle control, smooth steering, adaptive throttle regulation, and timely braking across varying traffic and weather conditions. The results further indicate significant improvements in driving safety, vehicle stability, collision avoidance, and computational efficiency compared with conventional autonomous driving approaches. The proposed hybrid architecture provides a scalable, cost-effective, and practical solution for next-generation intelligent autonomous vehicles operating in real-world environments.