2026-07-23 04:00 UTCOriginal source2 min readUpdated: 2026-07-23 08:39 UTC

D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

This paper introduces D3VL, a novel multimodal large language model framework that integrates 2D and 3D time-series data for autonomous driving scene understanding, achieving 11% improvement on the KITTI QA dataset and introducing a new Waymo QA extension.

SourcearXiv Computer VisionAuthor: Heesang Han, A. Lynn Abbott, Abhijit Sarkar

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[Submitted on 21 Jul 2026]

Title:D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

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Abstract: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)

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