CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

• URL: http://arxiv.org/abs/2511.13297v1
• Date: Mon, 17 Nov 2025 12:21:03 GMT
• Title: CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving
• Authors: Enhui Ma, Lijun Zhou, Tao Tang, Jiahuan Zhang, Junpeng Jiang, Zhan Zhang, Dong Han, Kun Zhan, Xueyang Zhang, XianPeng Lang, Haiyang Sun, Xia Zhou, Di Lin, Kaicheng Yu,
• Abstract summary: We introduce PM-Agent, which formulates data requirements to collect data similar to failure cases.<n>We then use a generative model that can simulate both data collection and annotation.<n>To address this, we propose DriveSora, which can generate consistent videos aligned with the 3D annotations requested by PM-Agent.<n>We integrate these components into our self-correcting agentic, system CorrectAD.
• Score: 32.564440450582346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 an 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.

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