{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:39:39Z","timestamp":1773801579094,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>End-to-end planning methods are the de-facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long-tail problem (i.e., rare but safety-critical failure cases). In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with structured 3D layouts, can enable a fully automated pipeline to self-correct such failure cases. We first introduce an agent to simulate the role of product manager, dubbed PM-Agent, which formulates data requirements to collect data similar to the failure cases. Then, we use a generative model that can simulate both data collection and annotation. However, existing generative models struggle to generate high-fidelity data conditioned on 3D layouts. To address this, we propose DriveSora, which can generate spatiotemporally consistent videos aligned with the 3D annotations requested by PM-Agent. We integrate these components into our self-correcting agentic system, CorrectAD. Importantly, our pipeline is end-to-end model agnostic and can be applied to improve any end-to-end planner. Evaluated on both nuScenes and a more challenging in-house dataset across multiple end-to-end planners, CorrectAD corrects 62.5% and 49.8% of failure cases, reducing collision rates by 39% and 27%, respectively.<\/jats:p>","DOI":"10.1609\/aaai.v40i10.37718","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:43:45Z","timestamp":1773791025000},"page":"7755-7763","source":"Crossref","is-referenced-by-count":0,"title":["CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving"],"prefix":"10.1609","volume":"40","author":[{"given":"Enhui","family":"Ma","sequence":"first","affiliation":[]},{"given":"Lijun","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Jiahuan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Junpeng","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Zhan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Zhan","sequence":"additional","affiliation":[]},{"given":"Xueyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xianpeng","family":"Lang","sequence":"additional","affiliation":[]},{"given":"Haiyang","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xia","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Di","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Kaicheng","family":"Yu","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37718\/41680","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37718\/41680","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:43:46Z","timestamp":1773791026000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37718"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i10.37718","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}