IJCAI 2021 AI4AD Workshop

Artificial Intelligence for Autonomous Driving

First Edition

August 20, 2021 | Montreal, Canada (Virtual)

Welcome to AI4AD

Autonomous driving provides a rich source of high-impact research problems for the broad artificial intelligence community across different fields such as computer vision, machine learning, robotics, language and speech, civil engineering, human-computer interaction, environmental science, and neuroscience. Further, full self-driving capability (“Level 5”) is far from solved and extremely complex, beyond the capability of any one institution or company, necessitating larger-scale communication and collaboration between researchers in different fields. The goal of this workshop is to embrace interdisciplinary knowledge in different fields of AI, from both academia and industry, to discuss how different fields can contribute to self-driving technology altogether and increase its social impact.


  • August 21, Thanks everyone for presenting and attending! We had a great workshop yesterday! Video recordings have been uploaded here and our YouTube channel

  • August 16, Please find our workshop in Green 3 area of the IJCAI virtual platform!

  • August 9, Full schedule of keynote and oral presentations has been announced here!

  • July 27, Video demo for accepted papers has been uploaded here!

  • June 27, Camera ready papers are available here! Video demo will be uploaded soon.

  • May 31, Paper decision has been sent out! A total of 12 submissions are accepted. Congratulations!

  • April 9, Paper submission opens. We feature two tracks: (1) short papers (≤ 4 pages); (2) regular papers.

  • March 25, Our workshop website is online.

  • March 15, Our workshop is accepted at IJCAI 2021!

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Topics Covered

  • Vision-based perception and scene understanding for autonomous driving

  • Multi-modality sensor fusion for autonomous driving

  • End-to-end and real-time autonomous driving systems

  • Novel automotive sensors and their applications

  • Behavior prediction of pedestrians, vehicles, and animals

  • Self/semi/weakly-supervised learning, domain adaptation for self-driving

  • Multi-task learning in autonomous driving

  • Explainability and interpretability in autonomous driving

  • Robustness to out-of-distribution road scenes

  • Learning to drive via imitation learning

  • Uncertainty propagation through autonomous driving pipelines

  • Planning and control for autonomous driving

  • Cooperative and competitive multi-agent systems

  • Visual grounding and its application to autonomous driving

  • Visual-language navigation for self-driving

  • Audio-visual navigation for self-driving

  • Auditory Perception (detection, tracking, motion estimation, etc)

  • Brain-inspired autonomous control systems

  • Human factors in autonomous driving

  • AI ethics in autonomous driving

  • Autonomous driving datasets and benchmarks

  • Evaluation and metrics of autonomous driving tasks

  • Connected autonomous driving and vehicle-to-vehicle communication

  • Autonomous driving for traffic management and emission reduction

Invited Speakers

(Alphabetical Order)

Senior Staff Engineer



University of Sydney

Senior Research Scientist


Ph.D. Student


Professor, UT Austin

Research Scientist, FAIR


KU Leuven

Chief Scientist


Machine Learning Scientist


Associate Professor

Cornell University

Staff Research Scientist / Manager