[visionlist] [CFP] Special Issue in Remote Sensing - Signal Processing and ML in AutoDriving

menna seyam menna.seyam at gmail.com
Fri Oct 14 15:12:10 -04 2022

Dear Colleagues,

Autonomous driving has attracted a multitude of research from the signal
processing, computer vision and machine/deep-learning communities. The
integration of signal processing, machine learning and advanced sensing
technologies is a key enabler for self-driving cars to operate in
real-world scenarios. The different types of sensors used, such as cameras,
LiDAR, GPS, radars, and ultrasound, and the ability to operate using
multiple modalities offer a wide variety of impactful research problems.
Machine learning research problems intersecting with the aforementioned
topics including perception, probabilistic modeling, future prediction,
path planning, and reinforcement-learning-based autodriving are also
investigated in the autonomous driving research. Finally, going beyond
benchmarks and deploying in real-world scenarios requires robustness, the
ability to operate with out-of-distribution scenarios and to take safety
into consideration. All the above has formed different research topics that
are of interest to the autonomous driving community in both academia and
industry, and in the intersection between signal processing and machine

The special issue
<https://www.mdpi.com/journal/remotesensing/special_issues/75B73YS791> welcomes
open-call submissions on the state-of-the-art current and emerging
technologies and methodologies in multi-modal learning, multi-sensor
utilization, and the interplay between signal-processing- and
learning-based approaches.

Remote Sensing Journal *Impact Factor is 5.349*. Deadline for manuscript
submissions: *30 June 2023*

Prospective authors are welcome to submit original research (not published
or currently under consideration by any other journal or conference) and
technical papers in the field. Topics in autonomous driving covered include:

   - X sensor (camera, LiDAR, radar, etc.)-based perception;
   - Multi-modal fusion and data fusion for autonomous driving;
   - Probabilistic modeling with multi-modal sensory input;
   - High-fidelity simulation for different sensory data;
   - Robustness to out-of-distribution scenarios;
   - Benchmarks and datasets with different sensory data;
   - Interpretability of multi-modal autodriving models;
   - Reinforcement-learning-based autodriving systems;
   - Enhanced path planning with sensory data processing;
   - Human factors and safety in autodriving.

Dr. Mennatullah Siam
Dr. Xinshuo Weng
Guest Editors

Best Regards,
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