The field of autonomous vehicles is witnessing significant advancements in perception and decision-making capabilities. Researchers are exploring innovative approaches to integrate computer vision, adaptive control, and machine learning to enhance the safety and efficiency of autonomous driving. The development of unified perception frameworks, which combine detection, tracking, and prediction tasks, is gaining traction. These frameworks have the potential to improve robustness, contextual reasoning, and efficiency while retaining interpretable outputs. Additionally, the use of vision foundation models and transformer architectures is being investigated to improve object detection and localization in the car interior and exterior environments. Noteworthy papers in this area include: SEER-VAR, which presents a novel framework for egocentric vehicle-based augmented reality that unifies semantic decomposition and LLM-driven recommendation. Interpretable Decision-Making for End-to-End Autonomous Driving, which proposes a method to enhance interpretability while optimizing control commands in autonomous driving. SKGE-SWIN, which utilizes the Swin Transformer with a skip-stage mechanism to broaden feature representation globally and at various network levels for end-to-end autonomous vehicle waypoint prediction and navigation.