# An interactive enhanced driving dataset for autonomous driving

arXiv: https://arxiv.org/abs/2602.20575 · HTML: https://mlsanity.com/paper/2602.20575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning VLA and VLM driving policies, one of 9 directions in End-to-end autonomous driving.
- First posted on arXiv 24 Feb 2026.

## Placed in

- End-to-end autonomous driving / [Reasoning VLA and VLM driving policies](https://mlsanity.com/driving/reasoning-vla-and-vlm-driving-policies/)

## Built on by (most cited first, 1 of 1)

- [SSP: An Event-Matched Syn2Sim2Phy Cross-Domain Evaluation Framework for Autonomous Driving VLA Models](https://mlsanity.com/paper/2608.14024/) (2026)

## Abstract (arXiv)

Driving interaction data are important for training and evaluating autonomous drivingVision-Language-Action (VLA) models, but existing datasets contain limited denseinteraction samples and weak alignment between trajectories, visual inputs, and languageannotations. This work presents the Interactive Enhanced Driving Dataset (IEDD), alarge-scale interaction-oriented dataset constructed from five naturalistic trajectory datasets:Lyft Level 5, Waymo, nuPlan, INTERACTION, and SIND. IEDD contains 7.31 millionego-centric interaction segments, including 6.66 million multi agents cases, covering head-on,car-following, merging, and crossing interactions. Each segment is associated withtrajectory-derived interaction metrics describing interaction intensity and efficiency. Based onthese annotations, IEDD-VQA further provides trajectory-reconstructed BEV videos,structured interaction semantics, and multi-turn question-answer pairs. The dataset cansupport interaction mining, long-tail scenario analysis, VLA instruction tuning, andhierarchical evaluation of perception, behavior description, physical quantification, andcounterfactual reasoning.