*** Please accept our apologies if you receive multiple copies of this CfPs ***
Early abstract submissions are required! Send your abstract (including tentative title, abstract, author list, and corresponding author affiliation and email) to Rui Fan (rui....@ieee.org) before January 29, 2023! If your abstract is within the scope of our special session, we will invite you to submit a full paper (4 pages). Please note: the paper review process for Special Session papers will be handled by the TPCs, along with the Regular Paper. The important dates and paper instructions are the same as Regular Paper. Call for Papers Due to the recent boom in artificial intelligence technologies, there are growing expectations that fully autonomous driving may become a reality in the near future and it is expected to bring fundamental changes to our society. Fully autonomous vehicles offer great potential to improve efficiency on roads, reduce traffic accidents, increase productivity, and minimize our environmental impact in the process. As a key component of autonomous driving, autonomous vehicle vision (AVVision) systems are typically developed based on cutting-edge computer vision, machine/deep learning, image/signal processing, and advanced sensing technologies. With recent advances in deep learning, AVVision systems have achieved compelling results. However, there still exist many challenges. For instance, the perception modules cannot perform well in poor weather and/or illumination conditions or in complex urban environments. Developing robust and all-weather visual environment perception algorithms is a popular research area that requires more attention. In addition, most perception methods are computationally-intensive and cannot run in real-time on embedded and resource-limited hardware. Therefore, fully exploiting the parallel-computing architecture, such as embedded GPUs, for real-time perception, prediction, and planning is also a hot subject that is being researched in the autonomous driving field. Furthermore, existing supervised learning approaches have achieved compelling results, but their performance is fully dependent on the quality and amount of labeled training data. Labeling such data is a time-consuming and labor-intensive process. Un/self-supervised learning approaches and domain adaptation techniques are, therefore, becoming increasingly crucial for real-world autonomous driving applications. Research papers are solicited in, but not limited to, the following topics: • 3D geometry reconstruction for autonomous driving; • Driving scene understanding; • Self-supervised/unsupervised visual environment perception; • Driver status monitoring and human-car interfaces; • Deep/machine learning and image analysis for autonomous vehicle perception; • Adversarial domain adaptation for autonomous driving. Organizers Dr. Rui Ranger Fan, Tongji University Dr. Wenshuo Wang, McGill University Important Dates Paper Submission Deadline: February 15, 2023 Paper Acceptance Notification: June 21, 2023 Final Paper Submission Deadline: July 5. 2023 Submission Paper Submission Instruction: https://cmsworkshops.com/ICIP2023/papers.php. The review process for Special Session papers will be handled by the TPCs, along with the Regular Paper. The important dates and paper instructions are the same as Regular Paper:
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