Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks (2505.02050v1)

Abstract: 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.

Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks

The paper "Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks" seeks to address the critical issue of cut-in maneuvers in high-speed traffic, which can lead to sudden braking and potential collisions. It introduces a Dynamic Bayesian Network (DBN) framework aimed at improving lane change predictions and ensuring safe cut-in maneuvers. This framework utilizes a probabilistic approach to assess lateral and longitudinal safety by integrating dynamic data regarding vehicle positions, velocities, relative distances, and Time-to-Collision (TTC) computations. The use of DBNs allows for a more robust and scalable safety validation of automated driving systems by overcoming the limitations of traditional heuristics and deterministic models.

The paper highlights the incorporation of three essential probabilistic hypotheses within the DBN framework: lateral evidence, lateral safety, and longitudinal safety. These elements are designed to facilitate real-time decision-making through dynamic evidence processing. The framework's implementation in the Joint Research Centre's Fuzzy Safety Model (JRC-FSM) simulator demonstrates a significant enhancement in crash avoidance performance compared to other conventional models like the Fuzzy Safety Model (FSM), Responsibility-Sensitive Safety (RSS), and baseline rule-based systems such as CC_human_driver and Reg157.

The efficacy of the proposed DBN framework is demonstrated through quantitative evaluations. The model achieved a crash avoidance rate improvement across various high-speed scenarios and maintained competitive performance in low-speed environments. Specifically, the DBN framework resulted in a reduction of crashes to 9.22% in high-speed scenarios compared to CC_human_driver and Reg157 models which recorded higher crash percentages. The integration of lateral evidence into the safety assessment model further enhances early recognition and mitigation of potential high-risk maneuvers, thereby optimizing safety margins.

The findings indicate that the DBN framework is not only effective in minimizing crash risks but also superior in maintaining safe vehicle operation under unpredictable traffic conditions. By employing DBNs, the framework can accommodate uncertainties inherent in real-world traffic, thus providing a pathway towards comprehensive safety solutions in autonomous driving technology.

Looking forward, the paper suggests potential directions for expanding this approach, including enhancing the reactive functions in the DBN model for adaptive speed control in dynamic traffic settings. This could potentially lead to more proactive decision-making frameworks, ultimately advancing the capabilities of autonomous systems in managing complex driving scenarios. The exploration underscores the practical utility of probabilistic modeling in developing rigorous safety standards in automated systems, paving the way for future research and innovation in the field of intelligent vehicles.