Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation

Topic Information

Dear Colleagues,

At present, the new unmanned vehicles technology and embodied intelligence systems for intelligent transportation are in a period of change. In the foreseeable near future, unmanned systems represented by UGV (Unmanned Ground Vehicle) and UAV (Unmanned Aerial Vehicle) will build new ground and air transportation, logistics, and operation systems, which will have great application potential in various fields of industry and agriculture. Unmanned driving systems (on the open road and the closed road) and intelligent agricultural machinery and equipment are representative intelligent transportation applications. 'Interactive' perception, 'learnable' cognition and decision making, and 'self-growth' behavior control are three important features of embodied intelligence. Correspondingly, multi-sensor (Lidar, millimeter wave radar, and optical sensor) and multi-source information fusion technology, SLAM technology, and bionic vision technology are applied to the perception stage. Brain-imitating intelligence and end-to-end deep learning neural networks are applied to the cognition and decision-making stage. Disturbance self-rejection control, integration control, bionic formation control, and manned/unmanned hybrid cooperative control technology are applied to the behavior control stage.

The scope of solicitation includes, but is not limited to, the following:

• Automatic driving, intelligent driving, and unmanned driving; embodied intelligence;
• Perception, cognition, and behavior;
• SLAM (Simultaneous Localization and Mapping);
• Lidar, millimeter-wave radar, RGB and RGB-D machine vision perception, and multi-spectral optical perception; 'interactive' perception;
• 'Learnable' cognition and decision making;
• 'Self-growth' behavior control; biologically inspired visual perception;
• Multi-sensor and multi-source information fusion;
• Brain-imitating intelligence and end-to-end deep learning neural networks;
• Disturbance observer and active disturbance rejection control;
• Perception, decision-making and control integration technology;
• Biologically inspired formation control;
• Hybrid cooperative control of manned/unmanned systems.

Dr. Jian Chen
Dr. Min Xia
Dr. Hui Xie
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.6	3.4	2014	22.3 Days	CHF 2400	Submit
Applied Sciences applsci	2.7	5.3	2011	16.9 Days	CHF 2400	Submit
Drones drones	4.8	5.6	2017	17.9 Days	CHF 2600	Submit
Electronics electronics	2.9	5.3	2012	15.6 Days	CHF 2400	Submit
Eng eng	-	2.1	2020	18.7 Days	CHF 1200	Submit
Remote Sensing remotesensing	5.0	8.3	2009	23 Days	CHF 2700	Submit
Sensors sensors	3.9	7.3	2001	17 Days	CHF 2600	Submit

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