Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

Large Language Models (LLMs) have shown promise in the autonomous driving sector, particularly in generalization and interpretability. We introduce a unique object-level multimodal LLM architecture that merges vectorized numeric modalities with a pre-trained LLM to improve context understanding in driving situations. We also present a new dataset of 160k QA pairs derived from 10k driving scenarios, paired with high quality control commands collected with RL agent and question answer pairs generated by teacher LLM (GPT-3.5). A distinct pretraining strategy is devised to align numeric vector modalities with static LLM representations using vector captioning language data. We also introduce an evaluation metric for Driving QA and demonstrate our LLM-driver's proficiency in interpreting driving scenarios, answering questions, and decision-making. Our findings highlight the potential of LLM-based driving action generation in comparison to traditional behavioral cloning. We make our benchmark, datasets, and model available for further exploration

Revealing the photophysics of gold-nanobeacons via time-resolved fluorescence spectroscopy

We demonstrate that time-resolved fluorescence spectroscopy is a powerful tool to investigate the conformation states of hairpin DNA on the surface of gold nanoparticles (AuNPs) and energy transfer processes in Au-nanobeacons. Long-range fluorescence quenching of Cy5 by AuNPs has been found to be in good agreement with electrodynamics modelling. Moreover, time-correlated single-photon counting (TCSPC) is shown to be promising for real-time monitoring of the hybridization kinetics of Au-nanobeacons, with up to 60% increase in decay time component and 300% increase in component fluorescence fraction observed. Our results also indicate the importance of the stem and spacer designs for the performance of Au-nanobeacons

Recent advances in cortical visual impairment

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66022/1/j.1469-8749.2001.tb00387.x.pd