Advancing Decision-Making in AI Through Bayesian Inference and Probabilistic Graphical Models

The navigation of autonomous vehicles should be accurate and reliable to navigate safely in changing and unpredictable conditions. This paper proposes an advanced autonomous vehicle navigation framework that integrates probabilistic graphical models, Markov Chain Monte Carlo methods, and Bayesian optimization to enable reliable, real-time decision-making in uncertain environments. Due to dynamic and unpredictable surroundings, autonomous navigation is highly challenged in uncertainty quantification and adaptive parameter tuning. By leveraging PGMs, the framework can first determine probabilistic dependencies between critical variables, i.e., nodes and edges, such as vehicle speed, obstacle proximity, and environmental factors, to create a robust foundation for situational awareness. Then, Bayesian inference is obtained using MCMC: the system updates its real-time beliefs as new sensor data become available. The inference layer allows adaptation to unexpected obstacles by revising trajectories or controlling a vehicle's speed while improving safety and reliability. Finally, Bayesian optimization fine-tunes key parameters within the system, such as sensor thresholds and control variables, maximizing efficiency without exhaustive manual tuning of these parameters. Using a multi-sensor data source with images, LiDAR, radar, and annotated environmental features, the Lyft Level 5 Perception Dataset tested real-world navigation scenarios against the framework. This proposed framework's accuracy was around 99.01% and signified good decision-making capabilities for an autonomous vehicle navigating through complex environments with reliable performance. The autonomous vehicle system is also intended to provide improved safety and flexibility in complex environments, promising the development of more resilient and dependable AI-driven solutions for navigation.