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

Zhiye Wang1*, Yanbo Jiang2*, Rui Zhou1, Bo Zhang3,4, Fang Zhang5†,
Zhenhua Xu2†, Yaqin Zhang3, Jianqiang Wang2,5
1School of Information Science and Engineering, Lanzhou University,
2The School of Vehicle and Mobility, Tsinghua University,
3The Institute for AI Industry Research (AIR), Tsinghua University, 4DiDi, 5State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University
* Equal contribution, † Corresponding author
Paper arXiv Code Data

Abstract

Large language models (LLMs) have shown great promise for autonomous driving, yet their discrete tokenization of numbers not only limits precise numerical reasoning but also introduces redundant decoding steps for each value, posing challenges for vehicle control. 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 continuous values as dedicated embeddings rather than discrete text tokens. DriveCode employs a number projector to map scalars 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.

Overview

Samples

We provide some data examples here.

Results

DriveCode consistently outperforms baselines across multiple datasets in both trajectory prediction and control signal generation tasks.

Trajectory Prediction on OmniDrive

Method L2 Error (m) ↓
Text 3.0797
DriveCode (Ours) 2.8274

Control Signals Prediction on DriveGPT4

Method Speed (m/s) Turning Angle (degree)
RMSE ↓ A0.1 ↑ A0.5 ↑ A1.0 ↑ A5.0 ↑ RMSE ↓ A0.1 ↑ A0.5 ↑ A1.0 ↑ A5.0 ↑
ADAPT 3.02 9.56 24.77 37.07 90.39 11.98 27.93 66.83 75.13 89.45
DriveGPT4 1.30 30.09 60.88 79.92 98.44 8.98 59.23 72.89 79.59 95.32
xVal 1.13 26.58 63.46 82.53 99.10 8.78 56.99 72.89 80.08 93.20
DriveCode (Ours) 1.08 27.50 64.60 82.99 99.10 7.71 57.18 72.54 80.25 93.71

Control Signals Prediction on DriveGPT4-V2

Method Theta Error (degree) ↓ Point Error (L2, m) ↓ Speed Error (m/s) ↓
xVal 0.07409 0.01166 0.02162
DriveCode (Ours) 0.07377 0.01137 0.02131

Contact

For any questions, please send email to wzhiye2023 at lzu dot edu dot cn.

Citation

@misc{wang2026drivecodedomainspecificnumerical,
      title={DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving}, 
      author={Zhiye Wang and Yanbo Jiang and Rui Zhou and Bo Zhang and Fang Zhang and Zhenhua Xu and Yaqin Zhang and Jianqiang Wang},
      year={2026},
      eprint={2603.00919},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.00919}, 
}