# Driving with InternVL: Oustanding Champion in the Track on Driving with Language of the Autonomous Grand Challenge at CVPR 2024

arXiv: https://arxiv.org/abs/2412.07247 · HTML: https://mlsanity.com/paper/2412.07247/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2024.

## Built on by (most cited first, 5 of 5)

- [VGGDrive: Empowering Vision-Language Models with Cross-View Geometric Grounding for Autonomous Driving](https://mlsanity.com/paper/2602.20794/) (2026)
- [Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation](https://mlsanity.com/paper/2512.18028/) (2025)
- [Unlock the Power of Unlabeled Data in Language Driving Model](https://mlsanity.com/paper/2503.10586/) (2025)
- [COVLM-RL: Critical Object-Oriented Reasoning for Autonomous Driving Using VLM-Guided Reinforcement Learning](https://mlsanity.com/paper/2512.09349/) (2025)
- [Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Driving](https://mlsanity.com/paper/2510.10503/) (2025)

## Abstract (arXiv)

This technical report describes the methods we employed for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge. We utilized a powerful open-source multimodal model, InternVL-1.5, and conducted a full-parameter fine-tuning on the competition dataset, DriveLM-nuScenes. To effectively handle the multi-view images of nuScenes and seamlessly inherit InternVL's outstanding multimodal understanding capabilities, we formatted and concatenated the multi-view images in a specific manner. This ensured that the final model could meet the specific requirements of the competition task while leveraging InternVL's powerful image understanding capabilities. Meanwhile, we designed a simple automatic annotation strategy that converts the center points of objects in DriveLM-nuScenes into corresponding bounding boxes. As a result, our single model achieved a score of 0.6002 on the final leadboard.