GPT-Driver: Learning to Drive with GPT (2310.01415v3)

Abstract: We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge in autonomous driving, aiming to plan a driving trajectory that is safe and comfortable. Existing motion planners predominantly leverage heuristic methods to forecast driving trajectories, yet these approaches demonstrate insufficient generalization capabilities in the face of novel and unseen driving scenarios. In this paper, we propose a novel approach to motion planning that capitalizes on the strong reasoning capabilities and generalization potential inherent to LLMs. The fundamental insight of our approach is the reformulation of motion planning as a LLMing problem, a perspective not previously explored. Specifically, we represent the planner inputs and outputs as language tokens, and leverage the LLM to generate driving trajectories through a language description of coordinate positions. Furthermore, we propose a novel prompting-reasoning-finetuning strategy to stimulate the numerical reasoning potential of the LLM. With this strategy, the LLM can describe highly precise trajectory coordinates and also its internal decision-making process in natural language. We evaluate our approach on the large-scale nuScenes dataset, and extensive experiments substantiate the effectiveness, generalization ability, and interpretability of our GPT-based motion planner. Code is now available at https://github.com/PointsCoder/GPT-Driver.

Overview of GPT-Driver: Learning to Drive with GPT

The paper "GPT-Driver: Learning to Drive with GPT" presents a novel approach that leverages OpenAI's GPT-3.5 model to enhance motion planning in the domain of autonomous driving. The core innovation lies in reconceptualizing motion planning not as a conventional data-driven task, but as a LLMing problem. This method underscores the potential of LLMs in addressing complex, real-world challenges traditionally reserved for different AI paradigms.

Key Contributions

The authors articulate several major contributions:

1. Reformulation of Motion Planning: The paper diverges from conventional heuristic and learning-based methods by redefining motion planning as a LLMing problem. Both inputs (e.g., vehicle states, maps) and outputs (i.e., driving trajectories) are mapped to sequences of language tokens, allowing the LLM to process and generate them as natural language descriptions. This approach exploits the inherent reasoning and generalization capabilities of GPT-3.5.
2. Prompting-Reasoning-Finetuning Strategy: This strategy is designed to augment the numerical reasoning ability of GPT-3.5. Through structured prompts, the model generates precise trajectory coordinates while articulating its decision-making process in natural language. This methodology not only improves the precision of generated trajectories but also enhances transparency and interpretability, two critical aspects in AI deployment in safety-critical domains like autonomous driving.
3. Empirical Validation: The approach was rigorously tested using the nuScenes dataset. Results indicate that GPT-Driver surpasses existing motion planners in human-like performance metrics, such as L2 error, while maintaining comparable collision rates. The model's performance remained robust even when trained with limited data, demonstrating notable generalization abilities.

Implications and Future Directions

The findings in the paper have significant implications both theoretically and practically. By introducing a novel intersection of LLMs and motion planning, the research invites a re-examination of LLM applicability across traditionally non-text-related fields. The fine-tuned version of GPT-3.5 shows capabilities that challenge the boundaries of its use-case scenarios, highlighting the model's potential in diverse AI applications beyond language processing.

Practically, this approach could simplify data processing pipelines in autonomous driving systems by embedding complex decision-making into a single model, reducing reliance on multiple specialized sub-components. Yet, an important consideration mentioned in the paper is the actual deployment in real-time scenarios due to potential computational overheads associated with large models like GPT-3.5. Future studies could focus on reducing inference time through model optimization techniques such as model distillation.

Moreover, the interpretability introduced through language-modeling paradigms contributes to safety and trust in AI systems, offering a template that could be adapted to other domains requiring transparency in decision-making.

In conclusion, "GPT-Driver" represents a meaningful step toward integrating LLMs into autonomous systems, unlocking new potentials for AI in complex, real-world environments. The paper posits future enhancements might include integrating additional sensory inputs and exploring close-loop evaluation techniques, a prospect that promises further improvements and refinements in the autonomous driving landscape.