Welcome to the 2023 Symposium on Machine Learning for Autonomous Driving collocated with NeurIPS!
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 workshops in 2016, 2017, 2018, 2019, 2020, 2021, 2022 enjoyed wide participation from both academia and industry.
Submission deadlines: 19th October 2023 and 16th November 2023, at 23:59 Anywhere on Earth
Submission website: https://cmt3.research.microsoft.com/ML4AD2023
Submission format: either extended abstracts (4 pages) or full papers (up to 9 pages) anonymously using:
• neurips_2023_ml4ad.tex -- LaTeX template
• neurips_2023_ml4ad.sty -- style file for LaTeX 2e
• neurips_2023_ml4ad.pdf -- example PDF output
Authors may submit to either submission round and reviewers will treat both rounds equally. Some authors may prefer the earlier notification date of the first round (27 Oct) while other authors might prefer a later submission deadline (16 Nov). References and appendix should be appended into the same (single) PDF document, and do not count towards the page count.
We invite submissions on machine learning applied to autonomous driving, including (but not limited to):
• Foundational Driving Models
• Prediction and Planning for AV with LLMs
• Mapless Autonomous Driving
• Scaling Laws for Autonomous Driving
• Diffusion modeling for prediction, planning
• Closed loop training and evaluation
• Causal/counterfactual analysis of interactive multi-agent scenarios
• Human driver in the loop for interaction modeling
• Coordination with vehicles (V2V) or infrastructure (V2I)
• Uncertainty propagation through AV software pipelines
• Imitation learning, Reinforcement learning for AV
• Transfer learning (Sim2Real) and domain adaptation for autonomous driving
• Off-road autonomous driving
• Real-time inference and prediction
• Adaptive driving styles based on user preferences
• Metrics/benchmarks for autonomous driving
Q: Are dual submissions OK?
Q: Will there be archival proceedings?
A: No. Submissions will 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.
The CARLA Autonomous Driving Challenge 2023 winners will present their solutions as part of the symposium. Details here.
Hilton Garden Inn New Orleans Convention Center, 1001 S Peters St, New Orleans, Louisiana 70130, United States
Aman Sinha firstname.lastname@example.org
is a research scientist at Waymo and co-founder of Trustworthy AI, which was acquired by Waymo in 2021.
Nigamaa Nayakanti email@example.com
is a research scientist at Waymo working on behaviour prediction modeling for autonomous vehicles.
Lars Kunze firstname.lastname@example.org
is a Departmental Lecturer in Robotics in the Oxford Robotics Institute and the Department of Engineering Science at the University of Oxford.
Maximilian Naumann Maximilian.Naumann@de.bosch.com
is a research engineer at Bosch Center for Artificial Intelligence.
Jiachen Li email@example.com
is a Postdoctoral Scholar at Stanford University working on scene understanding and decision making for intelligent systems.
Xinshuo Weng firstname.lastname@example.org
is a research scientist at NVIDIA Autonomous Vehicle Research.
Rowan McAllister email@example.com
is a staff research scientist at Waymo working planning for autonomous vehicles.
We thank Waymo and Toyota Research Institute for generously sponsoring this event.