Enhancing autonomous systems with bayesian neural networks: a probabilistic framework for navigation and decision-making

The rapid evolution of autonomous systems is reshaping urban mobility and accelerating the development of intelligent transportation networks. A key challenge in real-world deployment is the ability to operate reliably under uncertainty–arising from sensor noise, dynamic agents, and adverse weather conditions. This paper investigates Bayesian Neural Networks (BNNs) as a principled framework for uncertainty-aware decision-making in autonomous navigation. Through three representative case studies–urban navigation, obstacle avoidance, and weather-induced visual degradation–we demonstrate how BNNs outperform deterministic neural networks by providing calibrated predictions and uncertainty estimates. These probabilistic outputs enable conservative and interpretable decision-making in high-risk environments, thereby enhancing safety and robustness. Our results show that BNNs offer substantial improvements in trajectory accuracy, adaptability to occlusions, and resilience to perceptual distortion. This study bridges theoretical advances in Bayesian deep learning with practical implications for autonomous vehicles, establishing BNNs as a foundational tool for building safer and more trustworthy mobility systems.