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

doi:doi:10.57702/gddo6d3l

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

A driving scenario QA task and a 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).

BibTex:

@dataset{Long_Chen_and_Oleg_Sinavski_and_Jan_Hünermann_and_Alice_Karnsund_and_Andrew_James_Willmott_and_Danny_Birch_and_Daniel_Maund_and_Jamie_Shotton_2024,
    abstract = {A driving scenario QA task and a 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).},
    author = {Long Chen and Oleg Sinavski and Jan Hünermann and Alice Karnsund and Andrew James Willmott and Danny Birch and Daniel Maund and Jamie Shotton},
    doi = {10.57702/gddo6d3l},
    institution = {No Organization},
    keyword = {'GPT-3.5', 'QA pairs', 'RL agent', 'autonomous driving', 'driving scenarios'},
    month = {dec},
    publisher = {TIB},
    title = {Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving},
    url = {https://service.tib.eu/ldmservice/dataset/driving-with-llms--fusing-object-level-vector-modality-for-explainable-autonomous-driving},
    year = {2024}
}