{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:59:37Z","timestamp":1779314377303,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/270","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"2422-2430","source":"Crossref","is-referenced-by-count":1,"title":["GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving"],"prefix":"10.24963","author":[{"given":"Yunpeng","family":"Zhang","sequence":"first","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deheng","family":"Qian","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ding","family":"Li","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifeng","family":"Pan","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbao","family":"Liang","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingzong","family":"Liu","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhui","family":"Mei","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maolei","family":"Fu","sequence":"additional","affiliation":[{"name":"Geely Automobile Research Institute"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Ye","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhujin","family":"Liang","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Shan","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dalong","family":"Du","sequence":"additional","affiliation":[{"name":"PhiGent Robotics"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:34Z","timestamp":1758627214000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/270"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/270","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}