Machine Learning for Autonomous Driving

December 3rd, 2022


About

Welcome to the NeurIPS 2022 Workshop on Machine Learning for Autonomous Driving!

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 workshop 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 7th NeurIPS workshop in this series. Previous workshops in 2016, 2017, 2018, 2019, 2020 and 2021 enjoyed wide participation from both academia and industry.

Dates

Papers

Submission due: 1st October 2022, 23:59 Anywhere on Earth
Reviewing starts: 4th October 2022
Reviewing ends: 16th October 2022
Notification: 20th October 2022
Video upload: 1st November 2022
Camera Ready: 5th November 2022

Challenge

Training materials: 5th September 2022
Open: 19th September 2022
Close: 7th November 2022
Notification: 10th November 2022
Video submission: 24th November 2022

Workshop

Workshop event: December 3rd, 2022

Speakers

Submissions

Submission deadline: 1st October 2022 at 23:59 Anywhere on Earth
Submission website: https://cmt3.research.microsoft.com/ML4AD2022
Submission format: either extended abstracts (4 pages) or full papers (8 or 9 pages) anonymously using:
 •  neurips_2022_ml4ad.tex -- LaTeX template
 •  neurips_2022_ml4ad.sty -- style file for LaTeX 2e
 •  neurips_2022_ml4ad.pdf -- example PDF output
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):
 •  Robustness to out-of-distribution data and adversarial attacks
 •  Causal modeling for multi-agent traffic scenarios
 •  Safety assurance in autonomous driving
 •  Explainability/interpretability in autonomous driving
 •  Unsupervised representation learning for driving
 •  Supervised scene perception and classification
 •  Behavior/interaction modeling of traffic participants
 •  Coordination with human-driven vehicles
 •  Coordination with vehicles (V2V) or infrastructure (V2I)
 •  Uncertainty propagation through AV software pipelines
 •  Reinforcement learning for autonomous driving
 •  Imitation learning for autonomous driving
 •  Transfer learning from simulation to real-world (Sim2Real)
 •  Off-road autonomous driving
 •  Real-time inference and prediction
 •  Adaptive driving styles based on user preferences
 •  Action recognition for autonomous driving
 •  Metrics/benchmarks for autonomous driving

FAQ

Q: Will there be archival proceedings?
A: No. Neither 4 or 8 or 9 page 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.

Schedule

Saturday, December 3rd, 2022. All times are in Central Time. Current time is

Challenge




The CARLA Autonomous Driving Challenge 2022 winners will present their solutions as part of the workshop. Details here.

Organizers



  • Jiachen Li

    Jiachen Li   
    jiachen_li@stanford.edu

    is a Postdoctoral Scholar at Stanford University working on scene understanding and decision making for intelligent systems.

  • Nigamaa Nayakanti

    Nigamaa Nayakanti   
    nigamaa@waymo.com

    is a research scientist at Waymo working on behavior prediction and modeling for autonomous vehicles.

  • Xinshuo Weng

    Xinshuo Weng   
    xweng@nvidia.com

    is a Research Scientist at NVIDIA Autonomous Vehicle Research working on 3D computer vision and generative models in the context of autonomous driving.

  • Daniel Omeiza

    Daniel Omeiza   
    daniel.omeiza@cs.ox.ac.uk

    is a PhD student at the University of Oxford working on explainability in autonomous vehicles.

  • Ali Baheri

    Ali Baheri   
    ali.baheri@mail.wvu.edu

    is an assistant professor at West Virginia University working on machine learning, control, and data-driven optimization.

  • German Ros

    German Ros   
    german.ros@intel.com

    is the lead for Intel Autonomous Agents Labs.

  • Rowan McAllister

    Rowan McAllister   
    rowan.mcallister@tri.global

    is a research scientist at Toyota Research Institute working on probabilistic models and motion planning for autonomous vehicles.

Challenge Organizers

Program Committee

   We thank those who help make this workshop possible!