Workshop on Autonomous Driving (original) (raw)

Wednesday, June 3, 2026

Room 603, Colorado Convention Center, Denver, CO

About

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.

Program

Wednesday, June 3rd, 2026 (MDT, local time)

Room 603, Colorado Convention Center, Denver, CO
CVPR Virtual Website

News

• [June 8] Thanks to everyone contributing to the workshop! Raw recordings will be available to registered attendees soon on the CVPR virtual site. Edited recordings will become available in early September per CVPR guidelines.
• [May 29] Posters will be in Exhibit Hall A, Poster Boards 341 - 345. Authors are responsible for following format guidelines and putting up their posters.
• [Apr 8] The workshop will take place on Wednesday, June 3.
• [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.
• [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.

Challenges

Please 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.

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.

Call for Papers

Important Dates

• Workshop paper submission deadline:
  Friday, February 27, 2026
  Monday, March 2, 2026 (23:59 PST)
• Notification to authors:
  Friday, March 20, 2026
  Monday, March 23, 2026
• Camera ready papers and copyright forms due: Friday, April 10, 2026

Topics 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 topics
• Submitted manuscript should follow the CVPR 2026 paper template
• The page limit is 8 pages (excluding references)
• We do not accept dual submissions
• Submissions will be rejected without review if they:
  • contain more than 8 pages (excluding references)
  • violate the double-blind policy or violate the dual-submission policy
• The 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

Acknowledgement

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.

Accepted Papers

BePo: 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

Reviewers