Guest editorial

Autonomous vehicles are widely researched topic by many leading industries with the main focus on safety, reliability and performance. Autonomous vehicle technologies find applications in self-driving cars, drones, underwater vehicles and security applications. Autonomous vehicles need to provide superior performance and they have to react to the real-world conditions very fast to ensure safety. Reliability and safety of autonomous systems are improved recently by deploying data analytics at the edge rather than cloud. Autonomous vehicle framework needs to have machine learning and deep learning-based intelligent edge analytics to deal with the data generated from sensors and cameras. To enhance reliability and safety of autonomous vehicles, numerous sensors and cameras are required in the system design. Intelligent sensors and camera of autonomous vehicles require deep learning algorithms to process signal, image and video in a better way than existing computational solutions.

This special issue focused on applying artificial intelligence and edge computing for autonomous vehicles. The first paper titled “An improved rank criterion-based NLOS node detection mechanism in VANETs” focuses on reliable warning message delivery in vehicular ad hoc networks. The second paper “Camouflage detection with texture statistical characterization in autonomous systems” detects camouflaged objects in autonomous system-based military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments. The third paper “Obstacle-avoiding intelligent algorithm for quad wheel robot path navigation” proposes quad wheel robot with path navigation using an intelligent novel algorithm named as obstacle-avoiding intelligent algorithm. The fourth paper “Skeleton-based STIP feature and discriminant sparse coding for human action recognition” focuses on development of a human action recognition system for the unmanned environments. The final paper “Low-cost IoT framework for irrigation monitoring and control” presents a threshold value algorithm to optimize power consumption and to control irrigation process.

Dr S. Satheeskumaran received PhD degree in Information and Communication Engineering in 2016. Currently, he serves as a Professor in Department of Electronics and Communication Engineering, Anurag Group of Institutions, Hyderabad, India. He is a guest editor for Inderscience and Springer journals and served as a volume editor for Springer conference proceedings. He has research and teaching experience of more than 18 years and his research interests include Internet of things, machine learning, biomedical signal processing and Artificial intelligence-based healthcare applications. He received research grants from government and private funding agencies and recipient of best faculty award and emerging researcher award for his contribution toward teaching and research. He has published more than 50 research papers in SCI/Scopus journals and reputed conferences. He is a reviewer for IEEE, Elsevier, Springer, Taylor & Francis, Wiley and IGI Global journals and reviewed more than 200 research papers. He also served as keynote speaker, advisory committee member and session chair for IEEE/Springer international conferences.

Dr Yu-Dong Zhang received his PhD degree from Southeast University at 2010. He worked as postdoc from 2010 to 2012 in Columbia University, USA, and an assistant research scientist from 2012 to 2013 at Research Foundation of Mental Hygiene (RFMH), USA. He served as a full professor from 2013 to 2017 in Nanjing Normal University, where he was the director and founder of Advanced Medical Image Processing Group in NJNU. Now he serves as Professor in Department of Informatics, University of Leicester, UK from June 2017. He was included in “Most Cited Chinese researchers (Computer Science)” from 2014 to 2017. He published over 160 papers, and 16 were included in “ESI Highly Cited Papers” and two were included in “ESI Hot Papers”. His citation reached 9,286 in Google Scholar, and 4,943 in Web of Science. He is the Fellow of IET, and senior member of IEEE and ACM. He is the editor of Scientific Reports, IEEE Transactions on Circuits and Systems for Video Technology, etc. He served as the guest editor of Neural Networks, IEEE Transactions on Intelligent Transportation Systems, etc. He has conducted many successful academic grants and industrial projects.

Dr Danilo Pelusi received the PhD degree in Computational Astrophysics from the University of Teramo, Italy. Now he serves as associate professor at the Faculty of Communication Sciences, University of Teramo. He is an Associate Editor of IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, International Journal of Machine Learning and Cybernetics (Springer) and Array (Elsevier). He served as a guest editor for Elsevier, Springer and Inderscience journals, program member of many conferences and editorial board member of many journals. He is reviewer for many reputed journals such as IEEE Transactions on Fuzzy Systems and Neural Networks and Machine Leaning, and conferences such as IEEE Congress on Evolutionary Computation. His research interests include fuzzy logic, neural networks, information theory, machine learning and evolutionary algorithms.