D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models
Quick Answer
This paper shows that The D3VL framework integrates 2D and 3D time-series data for autonomous driving, achieving an 11% improvement on the KITTI QA dataset.
Quick Take
It addresses LiDAR data challenges and enhances traffic scene understanding, with a focus on safety. The Waymo QA dataset extension further evaluates performance under varied driving conditions.
Key Points
- D3VL improves performance on the KITTI QA dataset by 11% over baseline methods.
- The framework integrates LiDAR and stereo camera data for enhanced scene understanding.
- LiDAR data sparsity poses unique challenges in multimodal language model integration.
- Waymo QA dataset extension assesses model capabilities in diverse driving conditions.
- D3VL aims to improve safety in autonomous driving through better traffic scene analysis.
DeepSignal Analysis
What happened
The D3VL framework integrates 2D and 3D time-series data for autonomous driving, achieving an 11% improvement on the KITTI QA dataset. It specifically addresses challenges associated with LiDAR data, which is crucial for many autonomous systems. Additionally, the framework includes an extension to the Waymo QA dataset to evaluate performance across various driving conditions.
Key evidence
- D3VL shows an 11% improvement in the KITTI Question-Answering dataset compared to baseline methods.
- The framework integrates 2D and 3D time-series data, addressing LiDAR data challenges such as sparsity and lack of grid structure.
- The Waymo QA dataset extension assesses models' capabilities in processing 3D and time-series data under diverse driving conditions.
Why it matters
The integration of 3D data, particularly from LiDAR, into multimodal language models could enhance traffic scene understanding and safety in autonomous driving. This is significant as most autonomous systems rely heavily on LiDAR for sensing. The reported improvements in performance metrics suggest that D3VL could lead to more reliable and safer autonomous driving technologies.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving. However, the main emphasis to date has been for MLLMs using 2D images and videos. In contrast, this paper considers MLLM effectiveness using 3D sensors, particularly LiDAR and stereo cameras. LiDAR presents unique challenges to integration within an MLLM, largely because of data sparsity and lack of a grid structure for the data. For similar reasons, fusion of camera and LiDAR data within an MLLM pipeline is also uncommon. However, most autonomous systems rely on LiDAR-based sensing, and incorporating 3D data has been proven to improve performance in traditional 3D scene perception tasks. This paper presents D3VL, a novel MLLM framework that integrates 2D and 3D time-series data in a single but simple architecture. The model aims to answer questions involving traffic scene understanding and safety. D3VL shows an 11% improvement in the KITTI Question-Answering (QA) dataset compared to baseline methods in processing 2D and 3D time-series data. This paper further introduces the Waymo QA dataset extension, which assesses models' capabilities in processing 3D and time-series data under diverse driving conditions. D3VL implementation code and WaymoQA extension can be found on our supplemental website: this https URL
| Comments: | Accepted to IEEE IV 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.19528 [cs.CV] |
| (or arXiv:2607.19528v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19528 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Heesang Han [view email]
[v1]
Tue, 21 Jul 2026 19:19:03 UTC (10,822 KB)
— Originally published at arxiv.org
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