GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving
Yunpeng Zhang, Deheng Qian, Ding Li, Yifeng Pan, Yong Chen, Zhenbao Liang, Zhiyao Zhang, Yingzong Liu
Abstract
Modeling complicated interactions among the ego-vehicle, road agents, and map elements has been a crucial part for safety-critical autonomous driving. Previous work on end-to-end autonomous driving relies on the attention mechanism to handle heterogeneous interactions, which fails to capture geometric priors and is also computationally intensive. In this paper, we propose the Interaction Scene Graph (ISG) as a unified method to model the interactions among the ego-vehicle, road agents, and map elements. With the representation of the ISG, the driving agents aggregate essential information from the most influential elements, including the road agents with potential collisions and the map elements to follow. Since a mass of unnecessary interactions are omitted, the more efficient scene-graph-based framework is able to focus on indispensable connections and leads to better performance. We evaluate the proposed method for end-to-end autonomous driving on the nuScenes dataset. Compared with strong baselines, our method significantly outperforms in full-stack driving tasks.
BibTeX
@inproceedings{ijcai2025_graphadinteracti,
title = {GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving},
author = {Yunpeng Zhang and Deheng Qian and Ding Li and Yifeng Pan and Yong Chen and Zhenbao Liang and Zhiyao Zhang and Yingzong Liu and Jianhui Mei and Maolei Fu and Yun Ye and Zhujin Liang and Yi Shan and Dalong Du},
booktitle = {IJCAI 2025},
year = {2025}
}