Evaluation of Large Language Models for Decision Making in Autonomous Driving
This addresses the need for reliable decision-making in autonomous driving systems, though it appears incremental as it focuses on evaluating existing LLMs rather than proposing new methods.
The study quantitatively evaluated the spatial-aware decision making and traffic rule adherence capabilities of Large Language Models (LLMs) in autonomous driving, developing a system for a Proof of Concept to test feasibility in actual vehicles.
Various methods have been proposed for utilizing Large Language Models (LLMs) in autonomous driving. One strategy of using LLMs for autonomous driving involves inputting surrounding objects as text prompts to the LLMs, along with their coordinate and velocity information, and then outputting the subsequent movements of the vehicle. When using LLMs for such purposes, capabilities such as spatial recognition and planning are essential. In particular, two foundational capabilities are required: (1) spatial-aware decision making, which is the ability to recognize space from coordinate information and make decisions to avoid collisions, and (2) the ability to adhere to traffic rules. However, quantitative research has not been conducted on how accurately different types of LLMs can handle these problems. In this study, we quantitatively evaluated these two abilities of LLMs in the context of autonomous driving. Furthermore, to conduct a Proof of Concept (POC) for the feasibility of implementing these abilities in actual vehicles, we developed a system that uses LLMs to drive a vehicle.