Foundation Models for Autonomous Driving

Introduction

The 4th edition of the DriveX Workshop focuses on how foundation models and cooperative systems can redefine perception, prediction, planning, and decision-making for autonomous driving and intelligent transportation infrastructure.

Traditional single-vehicle pipelines have achieved impressive progress in 3D detection and tracking, yet they remain constrained by limited viewpoints, occlusions, and domain shifts. Cooperative driving systems, powered by V2X communication and roadside/edge intelligence, extend sensing range, enrich scene context, and enable shared representations across vehicles and infrastructure.

In parallel, foundation models, including vision, vision-language, and multi-modal large models, unlock powerful generalization capabilities: open-vocabulary understanding, scalable pretraining, zero-shot adaptation, and interpretable reasoning about complex road scenes. Emerging end-to-end and agentic systems such as large driving models promise unified perception-to-control frameworks but raise new questions in trustworthiness, reliability, calibration, and evaluation at urban scale.

DriveX 2026 convenes researchers and practitioners from computer vision, robotics, communications, transportation, AI safety, and policy to:

• Design foundation models natively aware of cooperative perception and real-world V2X constraints.
• Bridge large-scale datasets, simulation, and deployment for multi-agent coordination.
• Discuss standards, benchmarks, and open challenges for safe, human-centric cooperative autonomy.

Topics of Interest

• Foundation models for cooperative autonomous driving and intelligent transportation systems
• Vision-language models for traffic scene understanding and explanation
• LLM- and agent-based support for perception, prediction, planning, and V2X coordination
• Cooperative perception, V2X communication, and infrastructure-assisted driving
• 3D/4D Gaussian Splatting for 3D Scene Reconstruction
• Datasets, benchmarks, and evaluation protocols for foundation models and cooperative perception
• 3D occupancy, 3D detection, 3D semantic segmentation, and holistic 3D scene understanding
• End-to-end pipelines and large driving models for multi-agent decision-making
• Safety, robustness, uncertainty estimation, and open-set reasoning in cooperative systems
• Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) interaction and standards

Schedule (Tentative)

Time	Session
08:00 – 08:10	Opening Remarks – Welcome & Workshop Overview
08:10 – 08:30	Opening Keynote Keynote Dr. Walter Zimmer University of California Los Angeles (UCLA) & Technical University of Munich (TUM), USA Abstract Autonomous driving in urban environments is fundamentally limited by the range, occlusions, and failure modes of vehicle-only perception. This opening keynote shows research advances in cooperative roadside–vehicle perception by fusing multi-modal data from onboard sensors and intelligent roadside infrastructure via V2X communication to extend situational awareness beyond line of sight. The proposed methods improve real-time 3D object detection and tracking in dense traffic and are supported by new large-scale, multi-modal datasets for benchmarking cooperative perception in real-world urban settings. By integrating recent advances in foundation models, including vision-language models, the work further enables semantic understanding of complex traffic scenes, laying the groundwork for AI-driven urban digital twins and safer, more efficient intelligent transportation systems. Speaker Bio Dr. rer. nat. Walter Zimmer is a post-doctoral researcher at the University of California Los Angeles (UCLA) and guest researcher at the Technical University of Munich (TUM). He received his Ph.D. from the Technical University of Munich (TUM) in 2025. His research focuses on cooperative autonomous driving, 3D perception and 3D foundation models. He has authored over 40 publications at top venues such as CVPR, ICCV, ECCV, ICML, NeurIPS, and T-PAMI. Dr. Zimmer previously worked as an Autonomous Systems Engineer at the STTech startup and research assistant at Siemens AG. His work has earned multiple awards, including the IEEE ITSS Best Student Paper Award 2023 and IEEE ITSS Best Dissertation Award 2025.
08:30 – 08:50	Keynote 1 Keynote Balajee Kannan Motional, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
08:50 – 09:10	Keynote 2 Keynote Prof. Dr. Jiaqi Ma University of California Los Angeles (UCLA), USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
09:10 – 09:30	Keynote 3 Keynote Dr. Mingxing Tan Waymo, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
09:30 – 09:50	Sponsorship Advertisement Keynote Mustafa Bal NomadicML, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
09:30 – 10:00	Poster Session I & Coffee Break Posters Coffee Break
10:00 – 10:20	Keynote 4 Keynote Dr. Jamie Shotton Wayve, Vancouver Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
10:20 – 10:40	Keynote 5 Keynote Prof. Dr. Marco Pavone Stanford University & NVIDIA, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
10:40 – 11:00	Keynote 6 Keynote Dr. Phil Duan Tesla, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
11:00 – 11:30	Panel Discussion I: Industry Track Panel
11:30 – 12:00	Poster Session II & Coffee Break Posters Coffee Break
12:00 – 13:00	Lunch Break & Networking
13:00 – 13:20	Keynote 7 Keynote Prof. Dr. Bolei Zhou UCLA & Coco Robotics, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
13:20 – 13:40	Keynote 8 Keynote Prof. Dr. Manmohan Chandraker UCSD & NEC Labs, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
13:40 – 14:00	Keynote 9 Keynote Prof. Dr. Sharon Li University of Wisconsin-Madison, USA Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
14:00 – 14:30	Poster Session III & Coffee Break Posters Coffee Break
14:30 – 14:50	Keynote 10 Keynote Prof. Dr. Holger Caesar Delft University of Technology, Netherlands Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
14:50 – 15:10	Keynote 11 Keynote Prof. Dr. Daniel Cremers Technical University of Munich, Germany Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
15:10 – 15:30	Keynote 12 Keynote Prof. Dr. Angela Dai Technical University of Munich, Germany Abstract Abstract will be announced closer to the workshop date. Speaker Bio Speaker bio will be announced closer to the workshop date.
15:30 – 16:00	Panel Discussion II: Academic Track Panel
16:00 – 16:30	Poster Session IV & Coffee Break Posters
16:30 – 16:40	Oral Presentation 1 Oral Calibration-Free View-Agnostic Monocular 3D Object Detection for Urban Scenes
16:40 – 16:50	Oral Presentation 2 Oral 4-D Radar Meets LiDAR and Camera: Cooperative Perception under Adverse Weather
16:50 – 17:00	Oral Presentation 3 Oral Rethinking Intermediate Module Utilization in V2X End-to-End Autonomous Driving
17:00 – 17:10	Oral Presentation 4 Oral DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather
17:10 – 17:20	Oral Presentation 5 Oral GaussianDet3D: Bridging Gaussian Splatting and Sparse LiDAR Detection for Multi-View 3D Object Detection
17:20 – 17:30	Paper Awards Ceremony – Best Paper, Runner-Up, Best Application Paper, Best Poster & Best Keynote
17:30 – 17:40	Challenge Winner Presentation
17:40 – 17:50	Challenge Awards Ceremony
17:50 – 18:00	Closing Remarks & Group Photo
19:00 – 21:00	Workshop Reception & Networking Reception

Final schedule, room allocation, and speaker order will be announced closer to the workshop date.

Paper Track

DriveX 2026 invites high-quality contributions on foundation models, cooperative perception, large driving models, and related topics outlined above.

We welcome:

• Novel full papers (max. 8 pages, excluding references) for publication in the official proceedings.
• 4-page extended abstracts or 8-10 page versions of recently published work (non-archival, not included in proceedings).

Submissions must follow the official CVPR 2026 style: LaTeX or Typst.

📘 Archival Track (Proceedings Track)

• Paper Type: Full papers (max. 8 pages, excluding references)
• Included in Proceedings: Yes (archival)
• Submission Opens: February 1, 2026 (23:59 PST)
• Submission Deadline: March 1, 2026 (23:59 PST)
• Notification to Authors: March 10, 2026March 17, 2026
• Camera-Ready Deadline: March 20, 2026March 27, 2026

📗 Non-Archival Track

• Paper Type: 4-page extended abstracts or 8-10 page versions of recently published work
• Included in Proceedings: No (non-archival)
• Submission Deadline: May 1, 2026 (23:59 PST)
• Notification to Authors: May 10, 2026
• Camera-Ready Deadline: May 20, 2026

Paper Awards

• 🏆 Best Paper Award: $500
• 🏆 Best Paper Runner-Up Award: $300
• 🏆 Best Application Paper Award: $200
• 🏆 Best Poster Award Certificate
• 🏆 Best Keynote Presentation Award Certificate

Challenge Awards

• 🏆 TUMTraf-V2X Challenge Winner Award: $300
• 🏆 doScenes Challenge Winner Award: $300
• 🏆 MDrive Challenge Winner Award: $300

Second and third place of each challenge will receive an award certificate.

• V2I-Based Cooperative Perception
  Infrastructure–vehicle fusion using TUMTraf-V2X. Focus on cooperative 3D detection and tracking with infrastructure-mounted LiDAR, radar, and cameras, emphasizing occlusion handling, long-range awareness, and reliability under real-world conditions.

  BibTeX citation (TUMTraf)

  @INPROCEEDINGS{10658179,
    author={Zimmer, Walter and Wardana, Gerhard Arya and Sritharan, Suren and Zhou, Xingcheng and Song, Rui and Knoll, Alois C.},
    booktitle={2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    title={TUMTraf V2X Cooperative Perception Dataset},
    year={2024},
    volume={},
    number={},
    pages={22668-22677},
    keywords={Point cloud compression;Solid modeling;Three-dimensional displays;Computational modeling;Object detection;Road safety;Sensors;Autonomous Driving;Deep Learning;Cooperative Perception;3D Object Detection;V2X;Dataset;Camera-LiDAR Fusion;Roadside Infrastructure;ITS;Tracking;Fusion;Annotation Tool;Labeling Tool;Point Cloud;Image;V2I;LiDAR;Camera;GPS;IMU;Transformer;HD Map;Collaborative Perception;Vehicle-Infrastructure Fusion;TUMTraf;OpenLABEL},
    doi={10.1109/CVPR52733.2024.02139}
  }

• Natural Language Instruction for Human Interaction and Vision-Language Navigation
  Built upon doScenes. Participants design models for natural language instructions and visual language navigation to facilitate research on human-vehicle instruction interactions.

  BibTeX citation (doScenes)

  @inproceedings{roy2025doscenes,
    title={doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigation},
    author={Parthib Roy and Srinivasa Perisetla and Shashank Shriram and Harsha Krishnaswamy and Aryan Keskar and Ross Greer},
    booktitle={2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)},
    pages={1651--1658},
    year={2025},
    organization={IEEE},
    url={https://arxiv.org/abs/2412.05893},
  }

• Multi-Agent Reasoning
  Using MDrive teams explore a cooperative driving benchmark for end-to-end closed-loop multi-agent systems.

  BibTeX citation (MDrive)

  @misc{MDrive,
    author = {Marco Coscoy and Zhiyu Huang and Johnson Liu and Jiaqi Ma and Angela Magtoto and Rui Song and Henry Wei and Zhihao Zhao and Bolei Zhou and Zewei Zhou and Walter Zimmer},
    title = {MDrive: A Cooperative Driving Benchmark for End-to-End Closed-loop Multi-Agent System},
    year = {2026},
    howpublished = {\url{https://github.com/ucla-mobility/MDrive}}
  }

Competition Timeline

• Competition Announcement: Feb 9, 2026
• Competition Start: Feb 15, 2026 (10 weeks)
• Submission Deadline: May 8, 2026 (23:59 PST)
• Notification to Participants: May 10, 2026

Top-performing teams will be invited to present at the workshop and will receive money prizes ($300) and award certificates. Detailed rules, baselines, and submission instructions are available on the official challenge page.

Organizers

Dr. Walter Zimmer

University of California, Los Angeles & Technical University of Munich

Dr. Rui Song

University of California, Los Angeles & University of Cambridge

Prof. Ross Greer

University of California, Merced

Yun Zhang

University of California, Los Angeles

Dr. Xuewei (Tony) Qi

Motional AD LLC

Dr. Zhiyu Huang

University of California, Los Angeles

Tianhui Cai

University of California, Los Angeles

Seth Z. Zhao

University of California, Los Angeles

Zewei Zhou

University of California, Los Angeles

Zhaoliang Zheng

University of California, Los Angeles

Dr. Hu Cao

Technical University of Munich

Chuheng Wei

University of California, Riverside

Max Ronecker

Graz University of Technology & SETLabs

Dr. Xiangyu Chen

Waymo

Lena Wild

KTH & Stanford Uni.

Dr. Arpita Vats

LinkedIn

Xiangbo Gao

Texas A&M University

Dr. Haibao Yu

The University of Hong Kong

Rahul Raja

LinkedIn

Xingcheng Zhou

Technical University of Munich

Jiajie Zhang

Technical University of Munich

Dr. Stephany Berrio Perez

The University of Sydney

Dr. Mao Shan

The University of Sydney

Dr. Lars Ullrich

Friedrich-Alexander-Universität Erlangen-Nürnberg

Peizheng Li

University of Tübingen & Mercedes-Benz

Senthil Yogamani

Qualcomm

Prof. Zhengzhong Tu

Texas A&M University

Prof. Alina Roitberg

University of Hildesheim

Prof. Daniel Watzenig

Graz University of Technology

Prof. Guoyuan Wu

University of California, Riverside

Prof. Holger Caesar

Delft University of Technology

Prof. Bolei Zhou

University of California, Los Angeles and Coco Robotics

Prof. Olaf Wysocki

University of Cambridge

Prof. Jiaqi Ma

University of California, Los Angeles

Prof. Mohan Trivedi

University of California, San Diego

Prof. Alois C. Knoll

Technical University of Munich

Invited Program Committee

Sponsors

Motional is a leading autonomous driving technology company, established as a venture between Hyundai Motor Group and Aptiv, that develops Level 4 driverless vehicles designed for safe and accessible urban transportation. The company currently operates commercial robotaxi and delivery services using its all-electric Hyundai IONIQ 5 platform through high-profile partnerships with networks such as Uber and Lyft.

Nomadic's Visual AI automatically discovers rare edge cases, multi-agent interactions, and safety-critical scenarios from fleet-scale video and V2X sensor streams. The platform provides the high-signal training data and evaluation benchmarks that foundation models need to bridge simulation and real-world deployment, trusted by the world's leading autonomous vehicle and robotics companies.

Qualcomm is a leading technology company whose Snapdragon Ride platform combines high-performance automotive chips, AI-driven perception, and advanced driver-assistance software to enable scalable ADAS and automated driving capabilities for automakers worldwide. Its Snapdragon Ride Pilot system, co-developed with partners like BMW and integrated with real-world data, supports hands-free highway and urban driving scenarios while helping vehicle makers accelerate safety-focused automated driving solutions.

DriveX 2026 welcomes sponsorship from industry, startups, and institutions interested in foundation models, cooperative perception, simulation, and large-scale autonomous driving systems.

For sponsorship opportunities, please contact: wz@ucla.edu.