DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving
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 n...
| Authors | Zhiye Wang, Yanbo Jiang, Rui Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 March 2026 |
| Last updated at source | 1 August 2026 |
Models and datasets referencing this paper
- shiftwilliam/DriveCode-dataDataset
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