{"ID":2846789,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01768","arxiv_id":"2511.01768","title":"UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs","abstract":"Although transformers have demonstrated remarkable capabilities across various domains, their quadratic attention mechanisms introduce significant computational overhead when processing long-sequence data. In this paper, we present a unified autonomous driving model, UniLION, which efficiently handles large-scale LiDAR point clouds, high-resolution multi-view images, and even temporal sequences based on the linear group RNN operator (i.e., performs linear RNN for grouped features). Remarkably, UniLION serves as a single versatile architecture that can seamlessly support multiple specialized variants (i.e., LiDAR-only, temporal LiDAR, multi-modal, and multi-modal temporal fusion configurations) without requiring explicit temporal or multi-modal fusion modules. Moreover, UniLION consistently delivers competitive and even state-of-the-art performance across a wide range of core tasks, including 3D perception (e.g., 3D object detection, 3D object tracking, 3D occupancy prediction, BEV map segmentation), prediction (e.g., motion prediction), and planning (e.g., end-to-end planning). This unified paradigm naturally simplifies the design of multi-modal and multi-task autonomous driving systems while maintaining superior performance. Ultimately, we hope UniLION offers a fresh perspective on the development of 3D foundation models in autonomous driving. Code is available at https://github.com/happinesslz/UniLION","short_abstract":"Although transformers have demonstrated remarkable capabilities across various domains, their quadratic attention mechanisms introduce significant computational overhead when processing long-sequence data. In this paper, we present a unified autonomous driving model, UniLION, which efficiently handles large-scale LiDAR...","url_abs":"https://arxiv.org/abs/2511.01768","url_pdf":"https://arxiv.org/pdf/2511.01768v1","authors":"[\"Zhe Liu\",\"Jinghua Hou\",\"Xiaoqing Ye\",\"Jingdong Wang\",\"Hengshuang Zhao\",\"Xiang Bai\"]","published":"2025-11-03T17:24:19Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":607464,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2846789,"paper_url":"https://arxiv.org/abs/2511.01768","paper_title":"UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs","repo_url":"https://github.com/happinesslz/UniLION","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}