the few-show learning ability (Section 3.3), interpretability
(Section 3.4), compatibility (Section 3.5), and stability
(Section 3.6) of Agent-Driver. Finally, we conduct empirical
studies to evaluate the effectiveness of different components
and design choices of our approach in Section 3.7.
3.1. Experimental Setup
Dataset. The nuScenes [4] dataset is a large-scale and
real-world autonomous driving dataset that contains 1, 000
driving scenarios and approximately 34, 000 key frames
encompassing a diverse range of locations and weather
conditions. We follow the general practice and split the
whole dataset into training and validation sets. We utilize the
training set to train the neural modules and instruct the LLMs,
and we utilize the validation set to evaluate the performance
of our approach, ensuring a fair comparison with prior works.
Implementation details. We utilize gpt-3.5-turbo-0613 as
the foundation LLM for different components in our system.
ALanguageAgentforAutonomousDriving