Deep Learning for Bio-Engineering Applications in Automotive Field

Dear Colleagues,

This Special Issue welcomes submissions from all areas of the automotive field, with a special focus on research articles showing the development of advanced bio-inspired systems (both hardware and software) for addressing the main issues of the automotive market especially involving advanced technologies for autonomous and assisted driving (ADAS, i.e., advanced driver assistance systems).

There is growing interest in applying recent deep learning solutions to address different critical issues in the automotive field, including less-invasive and robust driving assistance systems, scene-understanding algorithms, predictive maintenance of automotive IPs, optimization of batteries and energy consumption, and secure communications between vehicles, to name but a few.

The advantages of using recent deep learning approaches has been highlighted in scientific studies published in recent years, as it is now possible to propose systems that significantly increase driving safety as well as to suitably adapt driving services to assist the car driver. Using modern, bio-inspired, self-attention and domain adaptation techniques, recent deep learning architectures are now able to outperform the methods previously proposed in relation to reconstruction and monitoring systems for driver attention, automotive sensing systems, driving scene reconstruction and understanding, pedestrian recognition and tracking, autonomous driving, robust semantic segmentation of the driving scene, driving recognition and prediction, car driver profiling, and numerous others.

In addition, the design of full solutions in addition to that of the developed algorithms implies an underlying hardware which must be capable of hosting the software deliverables (maintaining time, safety, and execution constraints required in the automotive solutions), which is of particular importance in this kind of application.

This Special issue brings together research papers which report new theoretical or applied advanced bio-inspired algorithms employing mathematical modeling and/or deep learning in tackling a variety of automotive issues. We strongly encourage the submission of papers that explore new research perspectives in different areas of automotives, including but not restricted to full bio-systems (hardware and software) for addressing classical automotive issues such as car driver profiling, car driver drowsiness monitoring, sensing, computer vision in automotive applications (scene understanding, autonomous driving, assisted driving, multimodal data fusion in automotives, etc.), radar or LiDAR applications, predictive maintenance, optimization of consumption and battery, characterization of innovative solutions for the electric car based on SiC (silicon carbide) and GaN (gallium nitride) technologies, analysis of physiological signals in the automotive field, GPS-based applications, and any other topics related to the theme of the Special Issue.

The Special Issue also welcomes replication and/or past published studies in any area of automotive field with the foresight that they are re-evaluated using alternative methods.

Prof. Dr. Francesco Rundo
Guest Editor

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• Deep learning systems for automotive applications
• Self-attention deep networks for signal and vision applications in automotives
• Driving scene understanding with domain adaption-based algorithms
• Embedded platforms for hosting deep learning algorithms for automotive applications
• Car driver profiling with deep learning
• Car driver drowsiness monitoring with deep learning in embedded systems
• Deep learning software and embedded microcontrollers for autonomous driving and assisted driving applications
• Deep Learning systems for real-time automotive applications
• Deep learning for improving SiC- and GaN-based solutions in electric cars
• Advanced deep learning systems for automotive sensing
• Advanced embedded platforms for hosting deep learning applications for ADAS applications
• Automotive-embedded deep learning
• Bio-inspired deep architectures for automotive applications
• Bio-inspired embedded systems for autonomous driving and assisted driving applications
• Bionic eyes platform (hardware and software) for safe and assisted driving
• Bio-inspired embedded infrastructure for secure communication between vehicles.