The rapid development of autonomous vehicles (AVs) demands robust
and adaptive AI systems capable of handling complex real-world
environments. Traditional optimization and learning algorithms often
struggle with dynamic and uncertain conditions, leading to suboptimal
decision-making. Swarm intelligence, particularly Hawk Fire
Optimization (HFO), offers a promising solution by simulating
cooperative behaviors seen in nature, like hawks in hunting, to optimize
decision-making processes. Coupled with advanced deep learning
techniques like Federated Dropout Learning (FDL), this hybrid
approach can enhance the adaptability, scalability, and efficiency of AI
systems. This paper addresses the challenge of improving decision-
making and learning in autonomous vehicles by integrating HFO with
FDL. HFO optimizes parameters in real-time, allowing AVs to adapt
rapidly to changing environments. Federated Dropout Learning, a
variant of federated learning, further improves system resilience by
sharing learning across distributed nodes while minimizing
communication overhead and enhancing privacy. By combining these
methods, the proposed system ensures robust performance in
unpredictable scenarios. Experimental results show that the hybrid
model outperforms traditional methods in terms of decision accuracy,
response time, and energy efficiency. Specifically, the system achieved
a 12% improvement in decision accuracy, reduced processing time by
18%, and cut energy consumption by 22%, compared to standard
algorithms. These findings suggest that the combination of HFO and
FDL can significantly improve the performance of autonomous
vehicles, providing safer and more efficient AI-driven navigation.