Overview
Building safe and intelligent Autonomous Vehicles (AVs) capable of human-like reasoning is a challenging problem, pushing the limits of computer vision. Current AV systems struggle with diverse and unseen driving scenarios, necessitating a shift in research focus. Recently, multimodal large language models (MLLMs) have shown great promise in understanding human intent and solving complex problems. Such models not only showcase incredible capabilities in understanding human intent and solving complex and unstructured problems, but scale gracefully with data and compute. This workshop explores leveraging MLLMs to tackle key challenges in AV.
Invited Speakers
Schedule
| Time | Event |
|---|---|
| 13:50 - 14:00 | Opening Remarks |
| 14:00 - 14:30 | Boris Ivanovic |
| 14:30 - 15:00 | Hongyang Li |
| 15:00 - 15:30 | Hang Zhao |
| 15:30 - 16:00 | Break |
| 16:00 - 16:30 | Long Chen |
| 16:30 - 17:00 | Katerina Fragkiadaki |
| 17:00 - 17:30 | Oral Session |
| 17:30 - 18:00 | Poster Session |
Call for Papers
We welcome authors to submit their papers in two different formats: full-paper (4-8 8-14 pages) or short-abstract (2 4 pages). A full-paper should describe work that has not been published or accepted to another venue. A short-abstract can highlight work that has been published or accepted recently. Please use the ECCV 2024 paper template and follow the ECCV submission guidelines. Accepted papers will be posted on the website, but there will not be archival proceedings for this workshop.
The submission needs to be submitted to the CMT system: https://cmt3.research.microsoft.com/MLLMAV2024.
Topics
- General system design of MLLMs for AV: The integration of MLLMs into AVs necessitates a reevaluation of data collection and usage, training and evaluation methodologies, and the overall system architecture.
- Perception: How can we leverage MLLMs to build more robust and powerful perception models in AV? Can we have a systematic way to deal with "tail" examples that are hard for traditional methods but easy for a human driver?
- Motion prediction: Can we use MLLMs to better understand the intents of other traffic participants and accurately forecast the movements of them?
- Trajectory planning: Can we use MLLMs to enable more sophisticated and adaptable planning algorithms that account for a wider range of variables and scenarios, leading to safer and more efficient navigation?
- Simulation and world models: Can MLLMs help generate more realistic and comprehensive simulation environments or build world models?
- End-to-end solutions: Can MLLMs play a crucial role in end-to-end AV solutions?
- Testing and safety: Can MLLMs make our AV system safer?
Important Dates
- Submission Open: June 25, 2024
- Submission Deadline:
August 15, 2024, 11:59 PM Pacific TimeAugust 25, 2024, 11:59 PM Pacific Time - Acceptance Decision: September 3, 2024
- Camera Ready Deadline: September 20, 2024
Accepted Papers
| Title and Authors | Link |
|---|---|
| Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection Authors: Mehar Khurana, Neehar Peri, James Hays, Deva Ramanan |
|
| Think-Driver: From Driving-Scene Understanding to Decision-Making with Vision Language Models Authors: Qiming Zhang, Meixin Zhu, Frank Yang |
|
| T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning Authors: Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers, Martin R. Oswald |
|
| Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models Authors: Yi Yang, Qingwen Zhang, Kei IKEMURA, Nazre Batool, John Folkesson |
|
| Distillation of Vision Language Models for Enhancing End-to-End Autonomous Driving Authors: Feng Tao, Abhirup Mallik, Chenbin Pan, Xin Ye, Yuliang Guo, Burhaneddin Yaman, Liu Ren |
