LLMs in Autonomous Driving — Part 2

Note: AI tools are used as an assistant in this post!

GPT-Driver: Learning to Drive with GPT

GPT-Driver paper proposes a novel approach to motion planning for autonomous vehicles that leverages the power of large language models (LLMs). The approach works by reformulating motion planning as a language modeling problem, where the planner’s inputs and outputs are represented as language tokens. An LLM, in this case GPT-3.5, is then used to generate driving trajectories through a language description of coordinate positions.

The paper also proposes a novel prompting-reasoning-finetuning strategy to stimulate the numerical reasoning potential of the LLM. This strategy enables the LLM to forecast highly precise waypoint coordinates and also articulate its internal decision-making process in natural language.

The authors evaluated their approach on the large-scale nuScenes dataset and found that it outperformed state-of-the-art motion planners in terms of effectiveness, generalization ability, and interoperability.

Overall, this paper presents a promising new approach to motion planning for…