The CVPR 2026 Workshop on Autonomous Driving (WAD) brings together leading researchers and engineers from academia and industry to discuss the latest advances in autonomous driving. Now in its 9th year, the workshop has been continuously evolving with this rapidly changing field and now covers all areas of autonomy, including perception, behavior prediction and motion planning. In this full-day workshop, our keynote speakers will provide insights into the ongoing commercialization of autonomous vehicles, as well as progress in related fundamental research areas. Furthermore, we will host a series of technical benchmark challenges to help quantify recent advances in the field, and invite authors of accepted workshop papers to present their work.
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[Apr 8] The workshop will take place on Wednesday, June 3.[Apr 1] The KITScenes LongTail Challenge is now online.[Mar 27] The 2026 Argoverse Scene Flow challenge is now online.[Mar 23] Final decisions were released to authors and accepted papers are now listed in the website.[Mar 20] Final decisions will now be released on Monday, March 23 to accommodate reviewing extensions.[Mar 3] Our paper track is now closed. Thanks to everyone submitting their work![Feb 24] We have extended the workshop paper submission deadline to Monday, March 2, 2026.[Feb 23] The 2026 Argoverse Scenario Mining challenge is now online.[Jan 29] We released our call for papers. Papers are due by Friday, February 27, 2026.[Dec 20] The workshop got accepted. More updates to follow soon.Call for Papers
Important Dates
- Workshop paper submission deadline:
Friday, February 27, 2026Monday, March 2, 2026 (23:59 PST)Notification to authors:Friday, March 20, 2026Monday, March 23, 2026Camera ready papers and copyright forms due: Friday, April 10, 2026Topics Covered
We invite submissions of original research contributions in machine perception, computer vision, prediction, planning and simulation related to autonomous vehicles, such as (but not limited to):
- Foundational models for autonomous driving.Vision language models (VLMs) and large language models (LLMs) for solving autonomous vehicle related tasks such as prediction or planning.Autonomous navigation and exploration based on camera, laser, radar or related measurements.Embodied AI for autonomous driving.Sensor fusion and multi-modal perception algorithms for scene understanding.Bird’s eye view methods for autonomous driving, such as BEV-based 3D detection, BEV segmentation, occupancy grids, HD-maps, and topological lane graphs.Vision-based driving assistance, driver monitoring and advanced interfaces.Sensor simulation, neural rendering / NeRFs, 3D Gaussian Splatting, generative models for 3D assets or driving environments.Diffusion models for prediction and planning.Mapless autonomous driving.Cooperative perception and planning based on vehicle-to-everything (V2X) / vehicle-to-vehicle communication.Transfer learning and domain adaptation in the autonomous vehicle domain.Simulation for autonomous driving.Online sensor calibration.SLAM and 3D reconstruction algorithms.Validation and interpretability of autonomous systems.Adversarial learning, adversarial attacks, robustness and handling of uncertainty in autonomous systems.
Presentation Guidelines
All accepted papers will be presented as posters. The guidelines for the posters are the same as at the main conference.Submission Guidelines
- We solicit short papers on autonomous vehicle topicsSubmitted manuscript should follow the CVPR 2026 paper templateThe page limit is 8 pages (excluding references)We do not accept dual submissionsSubmissions will be rejected without review if they:
- contain more than 8 pages (excluding references)violate the double-blind policy or violate the dual-submission policyThe accepted papers will be linked at the workshop webpage and also in the main conference proceedings.Papers will be peer reviewed under double-blind policy, and must be submitted online.
Submission Instruction
Submit your papers through CMT: https://cmt3.research.microsoft.com/WAD2026Acknowledgement
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.Tentative ScheduleFollow the livestream on the CVPR Virtual Website. Recordings will be published after the workshop.
09:15am09:30amOpening Remarks09:30am10:00amKeynote 1Title: To be announced10:00am10:30amKeynote 2Title: To be announced10:30am11:00amCVPR AM Coffee Break11:00am11:30amKeynote 3Title: To be announced11:30am12:00pmDataset Challenges12:00pm01:30pmLunch Break & Poster Session
Poster Location: To be announced01:30pm02:00pmKeynote 4Title: To be announced02:00pm02:30pmKeynote 5Title: To be announced02:30pm03:00pmCVPR PM Coffee Break03:00pm03:30pmKeynote 6Title: To be announced03:30pm04:00pmKeynote 7Title: To be announced04:00pm05:00pmLightning TalksTo be announced05:00pm05:05pmClosing Remarks
ChallengesPlease note: Challenges are not directly affiliated with the workshop. If you have any questions regarding a dataset challenge or encounter any issues, please contact the challenge organizers directly.
Argoverse Scenario Mining and LiDAR Scene Flow Challenges
The workshop will host the Argoverse 2026 challenges for Scenario Mining and LiDAR Scene Flow. To participate and more information, visit the Argoverse website.
KITScenes LongTail Challenge
The workshop will host the KITScenes LongTail Challenge, which focuses on the few-shot generalization of end-to-end driving models (e.g., VLAs and VLMs) in long-tail scenarios. The dataset is available on Hugging Face. Further details on prizes, metrics and helper functions can be found in the submission space.
Accepted PapersBePo: Dual Representation for 3D Occupancy Prediction
Authors: Yunxiao Shi, Hong Cai, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Amin Ansari, Fatih Porikli
CCLSTM: Coupled Convolutional Long-Short Term Memory Network for Occupancy Flow Forecasting
Authors: Peter Lengyel
CogAD: Cognitive-Hierarchy Guided End-to-End Autonomous Driving
Authors: Zhennan Wang, Jianing Teng, Canqun Xiang, Kangliang Chen, Xing Pan, Lu Deng, Weihao Gu
Edge-Efficient Vision-Language Models for Autonomous Driving Using Distillation and RAG-Based Connectors
Authors: Alexandra Chiu, Tanvi Aggarwal, Qidao Lian, Sabyasachi Gupta, Kevin Nowka
InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset
Authors: Felix Stillger, Lukas Hahn, Frederik Hasecke, Tobias Meisen
Localization-Guided Foreground Augmentation in Autonomous Driving
Authors: Jiawei Yong, Deyuan Qu, Qi Chen, Kentaro Oguchi, Shintaro Fukushima
R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation
Authors: William Ljungbergh, Bernardo Taveira, Wenzhao Zheng, Adam Tonderski, Chensheng Peng, Fredrik Kahl, Christoffer Petersson, Michael Felsberg, Kurt Keutzer, Masayoshi Tomizuka, Wei Zhan
SafeDrive: Improving Adverse-Weather Robustness in Autonomous Driving via Geometry-Aware Diffusion Augmentation
Authors: Syeda Fiza Rubab, Arslan Abdul Ghaffar, Ingyu Lee, Gyu Sang Choi
Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model
Authors: Bo-Kai Ruan, Hao-Tang Tsui, Yung-Hui Li, Hong-Han Shuai
When Does Adaptive Guidance Help? Belief-Aware Privileged Distillation for Autonomous Driving Under Partial Observability
Authors: Mehmet Haklidir
ReviewersName Affiliation Aakanksha Aakanksha Indian Institute of Technology Madras Abdelrahman O Ali Photon Smart Ahmed Abdelrahman University of Central Florida (UCF) Alberto G Rodriguez Salgado Technische Universität München Alessandro Paolo Capasso Ambarella VisLab Alexander Bienemann University of the Bundeswehr Munich Alperen Degirmenci NVIDIA Alperen Kantarcı Goethe University Frankfurt Anastasia Bolovinou ICCS Angelos Amanatiadis Democritus University of Thrace Anton Kuznietsov TU Darmstadt Arash Akbari Northeastern University Arslan Abdul Ghaffar Yeungnam University Benedikt Alt Robert Bosch GmbH Bharatesh Chakravarthi Arizona State University Bikram Adhikari Driver Research Institute Bingyin Zhao National University of Singapore Bo-Kai Ruan National Yang Ming Chiao Tung University Bolin Zhou China Automotive Technology and Research Center Co., Ltd. Ce Zhang Virginia Tech Cem Tarhan Togg Chieh-Chih Wang NCTU Deepak Ravishankar NVIDIA Deyuan Qu Toyota Dianwei Chen University of Maryland Douglas B. Cavalcante IPT Edmund K Chao University of California, Los Angeles Ehsan Ahmadi University of Alberta Elahe Yahyapour University of Massachusetts Amherst Eun Sang Cha Korea University Fanta Camara University of York Federico Camarda Heudiasyc Felix Stillger Bergische Universität Wuppertal Flavia Sofia Acerbo KU Leuven Frederik Lenard Hasecke Aptiv Gaël Parfait Atheupe Gatcheu Ensta Gibran Ali Virginia Tech Transportation Institute Giorgio C Buttazzo Scuola Superiore Sant'Anna Gyu Sang Choi Yeungnam University Haotian CAO National University of Defense Technology Hojin Ahn Korea Advanced Institute of Science and Technology (KAIST) Ingyu Lee Yeungnam University Javad Zolfaghari Bengar Computer Vision Center Jenny Schmalfuss University of Stuttgart Jialei Chen Nagoya University Jiawei Yong Toyota Motor Corporation Jingde Chen NVIDIA Johannes Betz Technical University of Munich Kailun Yang Hunan University Kuderna-Iulian Benta Babes-Bolyai University Lu Cao Honda Research Institute Japan Mahan Rafidashti Chalmers University of Technology Mahmut Yurt Stanford University Marcello Ceresini VisLab Mathieu Cocheteux Université de Technologie de Compiègne Md Zafar Anwar Mercedes-Benz R&D North America Michael Brunner Reutlingen University Michael Hubbertz Bergische Universität Wuppertal Naa Korkoi Addo University of Limerick Peter Lengyel aiMotive Rafid Mahmood NVIDIA Rahul Bhadani Vanderbilt University Rajeev Yasarla Qualcomm AI Research Royden Wagner KIT Ruihao Zeng The University of Sydney Runheng Zuo Shanghai Jiao Tong University Ruphan Swaminathan Ottonomy Inc Sabyasachi Gupta Texas A&M University Shounak Sural Carnegie Mellon University Shuai Zheng Cruise LLC Shuxuan Guo EPFL Simon de Moreau Mines Paris - PSL University Suraj Bhardwaj BharAI Lab Syeda Fiza Rubab Yeungnam University Tamás Matuszka aiMotive Tobias Meisen University of Wuppertal Vibashan VS Johns Hopkins University Vikram Anantha Lexington High School Weichao Zhuang Southeast University Weitao Zhou Tsinghua University Xiangrui Zeng Huazhong University of Science and Technology Xiaokai Bai Zhejiang University Xin Zhou Tongji University Xinglong Sun Stanford University Xuesong Bai Beihang University Xuming He ShanghaiTech University Xunjiang Gu University of Toronto Yezhi Shen Purdue University Yihan Zhong The Hong Kong Polytechnic University Yilun Chen Chinese University of Hong Kong Yisheng An Chang'an University Yu Han Lixiang Yug Ajmera Waymo Yunheng Xu Anhui University Yunxiao Shi Qualcomm AI Research Yuxiao Cao Huazhong University of Science and Technology Zhennan Wang Peng Cheng Laboratory Zi Wang NVIDIA
