{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/openad-open-world-autonomous-driving","title":"OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection","arxiv_id":"2411.17761","date":"2024-11-26","proceeding":null,"authors":["Zhongyu Xia","Jishuo Li","Zhiwei Lin","Xinhao Wang","Yongtao Wang","Ming-Hsuan Yang"],"abstract":"Open-world autonomous driving encompasses domain generalization and open-vocabulary. Domain generalization refers to the capabilities of autonomous driving systems across different scenarios and sensor parameter configurations. Open vocabulary pertains to the ability to recognize various semantic categories not encountered during training. In this paper, we introduce OpenAD, the first real-world open-world autonomous driving benchmark for 3D object detection. OpenAD is built on a corner case discovery and annotation pipeline integrating with a multimodal large language model (MLLM). The proposed pipeline annotates corner case objects in a unified format for five autonomous driving perception datasets with 2000 scenarios. In addition, we devise evaluation methodologies and evaluate various 2D and 3D open-world and specialized models. Moreover, we propose a vision-centric 3D open-world object detection baseline and further introduce an ensemble method by fusing general and specialized models to address the issue of lower precision in existing open-world methods for the OpenAD benchmark. Annotations, toolkit code, and all evaluation codes will be released.","url_abs":"https://arxiv.org/abs/2411.17761v1","url_pdf":"https://arxiv.org/pdf/2411.17761v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"openad-open-world-autonomous-driving","repo_url":"https://github.com/VDIGPKU/OpenAD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"multimodal-large-language-model","task_name":"Multimodal Large Language Model"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"open-world-object-detection","task_name":"Open World Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}