Machine Learning for Autonomous Driving

About

Welcome to the 2025 Workshop on Machine Learning for Autonomous Driving at AAAI.

Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this symposium aims to promote. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets. All are welcome to attend! This will be the 8th event in this series. Previous events in 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 enjoyed wide participation from both academia and industry.

Submissions

Submission deadlines: 24th November 2024, at 23:59 Anywhere on Earth
Submission website: https://cmt3.research.microsoft.com/ML4AD2025
Submission format: Papers should be anonymized; reviewing is double-blind. The requirements are analogous to AAAI requirements: 7 pages of technical content plus additional pages solely for references; acknowledgements should be omitted from papers submitted for review. Code and other supplementary materials can optionally be submitted; if using GitHub, please ensure that the repository is anonymous and has no information, including commit messages, that will break anonymity. Please use the AAAI style file https://aaai.org/authorkit25/

We invite submissions on machine learning applied to autonomous driving, including (but not limited to):
• Prediction and Planning for AD with LLMs
• Foundation Models for AD
• Mapless Autonomous Driving
• Scaling Laws for AD
• Diffusion modeling for prediction, planning
• Closed loop training and evaluation
• Causal/counterfactual analysis of interactive multi-agent scenarios
• Real-time inference and prediction
• Data-driven AD simulation
• Human driver in the loop for interaction modeling
• Coordination with vehicles (V2V) or infrastructure (V2I)
• Uncertainty propagation through AD software pipelines
• Imitation learning, Reinforcement learning for AD
• Off-road autonomous driving
• Adaptive driving styles based on user preferences
• Metrics/benchmarks for AD

FAQ

Q: Are dual submissions OK?
A: Yes.

Q: Will there be archival proceedings?
A: No. Submissions will not be indexed nor have archival proceedings.

Q: Should submitted papers be anonymized?
A: Yes. If accepted, we will ask for a de-anonymized version to link on the website.

Q: My papers contains ABC, but not XYZ, is this good enough for a submission?
A: Submissions will be evaluated based on these reviewer questions.