While the potential of reinforcement learning (RL) in automated driving systems is immense, it's not without its challenges. One of the major hurdles is creating precise reward functions that ensure safe, efficient, and human-like driving behavior. Despite this, RL's ability to adapt to various traffic conditions makes it a promising tool for developing intelligent driving systems.

Using large language models (LLMs) as a stand-in for reward signals introduces a novel approach. LLMs can intuitively steer RL agents toward desired outcomes by interpreting user goals into reward signals through natural language. This innovative method holds potential across multiple applications, such as automated driving and dynamic traffic management.