Technological progress in artificial intelligence, and specifically in the fields of deep learning and neural computing, has enabled outcomes of scientific research that would once have been considered almost impossible. In particular, modern intelligent approaches inspired by the biology of human neural networks permit the management of large amounts of data to be used for the implementation of various bio-engineering applications, including systems for both assisted driving and fully autonomous driving. These deep learning-based approaches leverage bio-inspired neural computing in order to address such critical challenges as driver attention-level monitoring, driving risk assessment, and enacting safe and robust driving strategies.
The advent of electric and hybrid cars, and the use of silicon carbide (SiC) devices and lithium-ion batteries, has introduced further issues to be addressed. Within the field of automotive engineering, there is now particular interest in the scientific investigation into solutions based on deep learning and neural computing for the prediction of the remaining useful battery life, as well as for defining predictive maintenance models for SiC devices in the car body.
The goal of this Research Topic is to collect scientific contributions that highlight the advantages and robustness of deep learning and bio-inspired neural computing for addressing the critical issues of modern automotive developments. More specifically, the aims of this Research Topic are: (a) to provide a comprehensive review of the most recent advanced bio-inspired neural computing and deep learning-based approaches for improving or proposing novel efficient solutions to complex problems in the modern automotive industry, and; (b) to investigate the multi-modal analysis of big data, signals and images for automotive applications.
Topics of interest include, but are not limited to:
- Bio-inspired neural systems for Advanced Driver Assistance Systems
- Neural computing for automotive applications
- Advanced neural motion magnification for computer vision systems in automotive applications
- Fractals, chaos and complexity in automotive applications
- Biologically inspired neural computing-based algorithms for biomedical signal processing in automotive applications
- Recent advances of deep learning in automotive applications
- Recent advances of deep learning in SiC device management for automotive applications
- Deep neural embedded systems for car driving assistance
- Neural embedded architectures for addressing challenges in electric car applications
- Novel deep architectures for addressing computer vision challenges in automotive applications
- Self-attention mechanisms in deep learning-based solutions for understanding driving scenarios
- Deep transformers for addressing critical issues in automotive applications
- Novel deep architectures for addressing electrical car body issues
- Neural computing for bio-sensing in automotive applications
- Bio-inspired neural systems for predictive maintenance in SiC automotive-grade devices
- Deep learning and self-attention for useful residual life-time prediction in automotive-grade devices
Reviews and surveys of the state of the art are also welcomed.
Topic Editor Dr Francesco Rundo is a Senior Researcher Technical Staff Member within the R&D Group of the Power and Discretes Division of STMicroelectronics. Topic Editor Dr Sabrina Conoci is R&D Manager of the Advanced Sensor Technologies team of STMicroelectronics. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Technological progress in artificial intelligence, and specifically in the fields of deep learning and neural computing, has enabled outcomes of scientific research that would once have been considered almost impossible. In particular, modern intelligent approaches inspired by the biology of human neural networks permit the management of large amounts of data to be used for the implementation of various bio-engineering applications, including systems for both assisted driving and fully autonomous driving. These deep learning-based approaches leverage bio-inspired neural computing in order to address such critical challenges as driver attention-level monitoring, driving risk assessment, and enacting safe and robust driving strategies.
The advent of electric and hybrid cars, and the use of silicon carbide (SiC) devices and lithium-ion batteries, has introduced further issues to be addressed. Within the field of automotive engineering, there is now particular interest in the scientific investigation into solutions based on deep learning and neural computing for the prediction of the remaining useful battery life, as well as for defining predictive maintenance models for SiC devices in the car body.
The goal of this Research Topic is to collect scientific contributions that highlight the advantages and robustness of deep learning and bio-inspired neural computing for addressing the critical issues of modern automotive developments. More specifically, the aims of this Research Topic are: (a) to provide a comprehensive review of the most recent advanced bio-inspired neural computing and deep learning-based approaches for improving or proposing novel efficient solutions to complex problems in the modern automotive industry, and; (b) to investigate the multi-modal analysis of big data, signals and images for automotive applications.
Topics of interest include, but are not limited to:
- Bio-inspired neural systems for Advanced Driver Assistance Systems
- Neural computing for automotive applications
- Advanced neural motion magnification for computer vision systems in automotive applications
- Fractals, chaos and complexity in automotive applications
- Biologically inspired neural computing-based algorithms for biomedical signal processing in automotive applications
- Recent advances of deep learning in automotive applications
- Recent advances of deep learning in SiC device management for automotive applications
- Deep neural embedded systems for car driving assistance
- Neural embedded architectures for addressing challenges in electric car applications
- Novel deep architectures for addressing computer vision challenges in automotive applications
- Self-attention mechanisms in deep learning-based solutions for understanding driving scenarios
- Deep transformers for addressing critical issues in automotive applications
- Novel deep architectures for addressing electrical car body issues
- Neural computing for bio-sensing in automotive applications
- Bio-inspired neural systems for predictive maintenance in SiC automotive-grade devices
- Deep learning and self-attention for useful residual life-time prediction in automotive-grade devices
Reviews and surveys of the state of the art are also welcomed.
Topic Editor Dr Francesco Rundo is a Senior Researcher Technical Staff Member within the R&D Group of the Power and Discretes Division of STMicroelectronics. Topic Editor Dr Sabrina Conoci is R&D Manager of the Advanced Sensor Technologies team of STMicroelectronics. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
