DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving

TL;DR

DriveCode introduces a new numerical encoding method for LLMs in autonomous driving, enabling precise numerical reasoning and efficient processing of sensor data and control commands.

Contribution

It proposes a novel embedding-based numerical encoding technique that improves numerical reasoning and integration in LLMs for autonomous driving tasks.

Findings

Outperforms existing methods in trajectory prediction

Enhances control signal accuracy

Demonstrates effectiveness across multiple datasets

Abstract

Large language models (LLMs) have shown great promise for autonomous driving. However, discretizing numbers into tokens limits precise numerical reasoning, fails to reflect the positional significance of digits in the training objective, and makes it difficult to achieve both decoding efficiency and numerical precision. These limitations affect both the processing of sensor measurements and the generation of precise control commands, creating a fundamental barrier for deploying LLM-based autonomous driving systems. In this paper, we introduce DriveCode, a novel numerical encoding method that represents numbers as dedicated embeddings rather than discrete text tokens. DriveCode employs a number projector to map numbers into the language model's hidden space, enabling seamless integration with visual and textual features in a unified multimodal sequence. Evaluated on OmniDrive, DriveGPT4, and DriveGPT4-V2 datasets, DriveCode demonstrates superior performance in trajectory prediction and control signal generation, confirming its effectiveness for LLM-based autonomous driving systems.