DriveX đźš— (3rd Edition)

Workshop on Foundation Models for

V2X-Based Cooperative Autonomous Driving

In conjunction with ITSC 2025, Nov 18, Gold Coast, Australia

Introduction

The 3rd edition of the full-day workshop explores advances in Foundation Models, Cooperative Autonomous Driving and 3D Perception. Topics include V2X communication, cooperative perception, 3D object detection, semantic segmentation, sensor fusion, and Vision-Language Models (VLMs). V2X communication enables vehicles and roadside infrastructure to share real-time information, improving situational awareness, extending sensory range, and providing early warnings for hazards. Cooperative perception addresses limitations of single-vehicle perception by fusing sensor data from multiple sources, which is crucial for understanding complex traffic scenarios. Foundation models, such as VLMs, have demonstrated strong generalization abilities by leveraging large-scale, cross-domain data. These models enable zero-shot learning, open-vocabulary object recognition, and scene interpretation, allowing autonomous vehicles to better handle unseen objects and novel traffic situations. Furthermore, we also explore Large Language Models (LLMs) to enhance perception accuracy, dataset curation, and novelty detection. By uniting experts across perception, V2X, and foundation model domains, this workshop aims to foster innovation in cooperative autonomous driving and intelligent transportation systems. Previous workshops editions are available here: https://drivex-workshop.github.io

Topics

3D Environment Perception
  • 3D Scene Understanding
  • 3D Occupancy Prediction
  • 3D Instance Segmentation
  • 3D Detection and Tracking
Cooperative Perception
  • V2X Communication
  • Vehicle-Infrastructure Fusion
  • Roadside & ITS Sensors (RSUs)
  • Multi-modal Sensor Data Fusion
Foundation Models (FMs)
  • LLM-assisted Perception & Prediction
  • Vision-Language Models (VLMs)
  • FMs for Dataset Curation & Labeling
  • FMs for Accident & Novelty Detection

Schedule

09:00 - 09:10 Opening Remarks (Welcome & Introduction)
09:10 - 09:30 Keynote 1
09:30 - 09:50 Keynote 2
09:50 - 10:10 Keynote 3
10:10 - 10:25 Coffee Break
10:25 - 10:45 Keynote 4
10:45 - 11:05 Keynote 5
11:05 - 11:25 Keynote 6
11:25 - 12:00 Academic Panel Discussion
12:00 - 13:00 Lunch Break
13:00 - 13:20 Keynote 7
13:20 - 13:40 Keynote 8
13:40 - 14:00 Keynote 9
14:00 - 14:20 Keynote 10
14:20 - 15:00 Industry Panel Discussion
15:00 - 15:15 Coffee Break
15:15 - 15:30 Oral Paper Presentation 1
15:30 - 15:45 Oral Paper Presentation 2
15:45 - 16:00 Oral Paper Presentation 3
16:00 - 16:15 Oral Paper Presentation 4
16:15 - 16:30 Oral Paper Presentation 5
16:30 - 16:45 Oral Paper Presentation 6
16:45 - 17:00 Competition Winner Presentation & Awards
17:00 - 17:15 Best Paper Presentation & Awards
17:15 - 17:25 Final Remarks & Summary
17:25 - 17:30 Group Picture (with all organizers and speakers)
17:30 - 18:00 Poster Session (20 Poster Presentations)
18:00 - 20:00 Social Mixer, Networking & Dinner

Invited Keynote Speakers from Academia












Invited Keynote Speakers from Industry









Paper Track

We accept novel full 8-page papers for publication in the proceedings, and either shorter 4-page extended abstracts or 8-page papers of novel or previously published work that will not be included in the proceedings. Full papers should use the official LaTeX or Typst IEEE template. For extended abstracts we recommend to use the same template.

Paper Awards

Challenge

We host multiple challenges based on our TUM Traffic Datasets, e.g. TUMTraf V2X Cooperative Perception Dataset (CVPR'24) which includes high-quality, real-world V2X perception data for the cooperative 3D object detection and tracking task in autonomous driving. The datasets are available here. We provide a dataset development kit to work with the dataset.

Competition Timeline:
The best-performing teams will be invited to present their solutions during the workshop, and the winners will receive prizes and recognition for their contributions to the field.

Challenge Awards

Invited Program Committee (TBD)

Sponsors

We are currently seeking for further sponsorship opportunities and would be delighted to discuss potential collaborations. Interested parties are kindly requested to contact us via email at walter.zimmer@cs.tum.edu for further details.