This paper introduces an adaptive lane-changing behavior learning agent designed for autonomous vehicles. The agent employs machine learning techniques to adaptively learn and optimize lane-changing decisions based on real-time traffic conditions and environmental cues. By leveraging reinforcement learning algorithms and sensor data, the agent continuously refines its decision-making process to navigate safely and efficiently through complex traffic scenarios. The paper discusses the implementation details of the learning agent and explores its practical applications in autonomous driving systems. Through simulation studies and real-world experiments, the effectiveness and robustness of the adaptive lane-changing behavior learning agent are evaluated, demonstrating its potential to enhance traffic flow, improve safety, and optimize driving efficiency in diverse road environments.

Effective access control in business processes requires a nuanced understanding of contextual constraints to ensure the integrity and security of organizational workflows. This paper proposes a holistic approach to business process access control, emphasizing the integration of contextual factors into access management strategies. By considering dynamic attributes such as user roles, environmental conditions, task dependencies, and organizational policies, the proposed framework aims to adapt access control decisions in real-time to accommodate changing business requirements and mitigate security risks. Drawing upon principles of adaptive access control and contextual reasoning, the framework seeks to enhance flexibility, scalability, and resilience in managing access to critical business processes. Through a comprehensive analysis of contextual constraints and their implications for access control, this paper offers insights into the design and implementation of robust access management systems tailored to the specific needs of modern organizations.