InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving

Paper

InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving

Ruiqi Song*, Xianda Guo*, Yanlun Peng, Hangbin Wu$\dagger$, Qinggong Wei, Long Chen$\dagger$

* Equal contribution $\dagger$ Corresponding author

News

• [2025/5/07] README.md Release

Overview

![overview]

• We present InsightDrive, which leverages CoT instructions to fine-tune LLMs and establishes a human–LLM–vehicle distillation pipeline that transfers human driving cognition into onboard models for joint explicit and implicit scene representation.
• We design a Task-level Mixture-of-Experts adapter that injects human driving cognitive processes into scene representations with minimal parameter overhead, which enhances scene understanding and reasoning.
• We propose a diffusion-based generative planner that uses explicit attention and implicit reasoning as conditions for generating robust and adaptive trajectories.
• We conduct comprehensive experiments on both the nuScenes and Navsim benchmarks, which demonstrate the effectiveness and robustness of InsightDrive and show that it achieves state-of-the-art performance.

FrameWork

Result

Visualization

Related Projects

Also thanks to these excellent open-sourced repos: GenAD

Citation

If you find this project helpful, please consider citing the following paper:

@article{insightdrive2025,
    title={InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving},
    author={Ruiqi Song and Xianda Guo and Hangbin Wu and Qinggong Wei and Long Chen },
    journal={https://arxiv.org/abs/2503.13047},
    year={2025}
}