The field of autonomous vehicle interaction and traffic management is moving towards more transparent and non-strategic frameworks for influence in human-robot interaction, enhancing both safety and efficiency. Researchers are exploring the application of Bayesian persuasion and large language models to improve communication at traffic intersections and predict ridesourcing mode choices. Additionally, there is a growing focus on assistive decision-making for right of way navigation at uncontrolled intersections and the use of multimodal large language models for detecting and describing traffic accidents. Noteworthy papers include: Trust-Aware Embodied Bayesian Persuasion for Mixed-Autonomy, which proposes a framework for transparent and non-strategic influence in human-robot interaction. Synthesizing Attitudes, Predicting Actions, which introduces a hierarchical approach using large language models to synthesize theory-grounded latent attitudes and predict ridesourcing choices. Investigating Traffic Accident Detection Using Multimodal Large Language Models, which evaluates the zero-shot capabilities of multimodal large language models for detecting and describing traffic accidents. iFinder, which introduces a structured semantic grounding framework for grounding large language models in domain-specific tasks like post-hoc dash-cam driving video analysis. Large Language Models for Pedestrian Safety, which leverages multimodal large language models to predict driver yielding behavior at unsignalized intersections.