Machine Vision and Intelligence for Unmanned Vehicles in the Real World Call for Papers

[CFP] Frontiers in Robotics & AI Special Issue: 
Machine Vision & Intelligence for Unmanned Vehicles in the Real World

Submission Deadlines: 
Dec. 17, 2021 -- Abstract
Mar. 25, 2022 -- Manuscript

Special Issue link:


In recent years, the presence of autonomous robots in the real world,
for example, self-driving cars, drones, and unmanned surface vehicles
have significantly increased. With the recent advances in machine/deep
learning, there are growing expectations that full autonomy may become
a reality shortly, and it is expected to bring fundamental changes to
the societies of robotics, computer vision, and artificial

An autonomous system typically consists of a series of modules
comprising perception, navigation, planning, and control. The
perception system is responsible for estimating location and
constructing the 3-D environment map to plan safe navigation
routes. With recent advances in machine/deep learning, such as
convolutional neural networks, autonomous robotsí perception,
navigation, and planning, robots have become more intelligent than
ever before, and such systems' applications are being realized.

This Research Topic aims to present current directions in this field
and explores the problems related to machine vision and intelligence
for autonomous systems in the real world. Specifically, this Research
Topic will mainly focus on:

1. Affordable sensors for varying environmental conditions;

2. Reliable simultaneous localization and mapping;

3. Machine learning that can effectively handle varying real-world
conditions and unforeseen events;

4. Hardware and software co-design for efficient real-time

5. Resilient and robust platforms that can withstand adversarial
attacks and failures;

6. End-to-end system integration of sensing, computer vision,
signal/image processing, and machine/deep learning.

In this way, relevant themes for this Research Topic include, but are
not limited to:

* 3D environment reconstruction and understanding;
* Mapping and localization for unmanned vehicles in the real world;
* Semantic/instance segmentation and semantic mapping;
* Self-supervised/unsupervised visual environment perception;
* Obstacle detection/tracking and 3D localization;
* Signage detection and recognition;
* Deep/machine learning and image analysis for intelligent environment perception;
* Adversarial domain adaptation for autonomous systems;
* On-board embedded visual perception systems;
* Bio-inspired vision sensing for autonomous system perception;
* Real-time deep learning inference.


Perception, navigation, planning, unmanned vehicles, AI

Important Note: 

All contributions to this Research Topic must be within the scope of
the section and journal to which they are submitted, as defined in
their mission statements. Frontiers reserves the right to guide an
out-of-scope manuscript to a more suitable section or journal at any
stage of peer review.

Guest Editors:

Rui Fan, Tongji University

Nan Li, Northwestern Polytechnical University

Mohammud J. Bocus, University of Bristol

Yuxiang Sun, Hong Kong Polytechnic University

Yue Wang, Zhejiang University


Rui Fan,