The field of autonomous driving and driver modeling is rapidly evolving, with a focus on developing more accurate and reliable models of driver behavior. Recent research has emphasized the importance of incorporating large language models, reinforcement learning, and conformal prediction to improve the safety and efficiency of autonomous vehicles. Additionally, there is a growing interest in using data-driven approaches to model human drivers' decision-making processes, including lane-changing and intersection navigation. These advancements have the potential to significantly enhance the performance and safety of autonomous vehicles, and are likely to play a key role in the development of next-generation transportation systems. Noteworthy papers include Akkumula, which introduces an evidence accumulation modeling framework using Spiking Neural Networks, and SafePath, which proposes a modular framework for safe LLM-based autonomous navigation using conformal prediction. Furthermore, the Internal State Estimation in Groups via Active Information Gathering paper presents a method for estimating human internal states in group settings, which has potential applications in social navigation and autism diagnosis.