Finally, top-K samples with the K highest similarity
scores are selected as candidates for the second-stage search.
In the second stage, we propose an LLM-based fuzzy
search. A detailed example of this stage is illustrated in

Figures 3 and 4. The top-K past driving scenarios selected
in the first stage are provided to the LLM. Then, the LLM is
tasked to understand the text descriptions of these scenarios
and determine the most similar past driving scenario to the
query scenario. The driving trajectory corresponding to the
selected scenario is also retrieved for reference.

With the proposed vector search and LLM-based fuzzy
search, Agent-Driver can effectively retrieve the most similar
past driving experience. The past experience and driving
decision could help the current decision-making process.

4. Reasoning Engine
ALanguageAgentforAutonomousDriving