Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks

--Cut-in maneuvers in high-speed traffic pose critical challenges that can lead to abrupt braking and collisions, necessitating safe and efficient lane change strategies. We propose a Dynamic Bayesian Network (DBN) framework to integrate lateral evidence with safety assessment models, thereby predicting lane changes and ensuring safe cut-in maneuvers effectively. Our proposed framework comprises three key probabilistic hypotheses (lateral evidence, lateral safety, and longitudinal safety) that facilitate the decision-making process through dynamic data processing and assessments of vehicle positions, lateral velocities, relative distance, and Time-to-Collision (TTC) computations. The DBN model's performance compared with other conventional approaches demonstrates superior performance in crash reduction, especially in critical high-speed scenarios, while maintaining a competitive performance in low-speed scenarios. This paves the way for robust, scalable, and efficient safety validation in automated driving systems. I NTRODUCTION The presence of advanced autonomous vehicles(A Vs) in real-world traffic is increasing daily, necessitating the need for robust models that can estimate risks and plan maneuvers proactively to ensure safety. Accurate detection and prediction of lane change maneuvers are crucial for collision avoidance, traffic flow optimization, and safety enhancement [2].