Deep Ensembles for Semantic Segmentation on Road Detection

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Application of a Brain-Inspired Deep Imitation Learning Algorithm in Autonomous Driving

Acknowledgements This work was was supported by the University of Aberdeen Internal Funding to Pump-Prime Interdisciplinary Research and Impact under grant number SF10206-57Peer reviewedPublisher PD

LLEDA - Lifelong Self-Supervised Domain Adaptation

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Smartphone-based extendable telematic data collection app

Funding Information: We extend our heartfelt gratitude to the individuals and organizations that made this research endeavour possible. First and foremost, we would like to acknowledge the voluntary efforts of the drivers at De-prize Motors and others at Etegwe Roundabout Motor Park, Yenagoa Bayelsa State Nigeria. We also extend our sincere appreciation to Mr. Kabiru Momodu, a key collaborator in this research project. His tireless efforts in mobilizing and coordinating drivers, as well as his commitment to the project's success, played a pivotal role in data collection and use of the software in a real world scenario. Furthermore, we would like to express our gratitude to the Tertiary Education Trust Fund (TetFund) for their generous sponsorship of this research. Their support made it possible to undertake the bigger PhD research project, focusing on the use of AI/NLG-enabled mobile apps for driving Behaviour change and the promotion of safe driving practices in Nigeria.Peer reviewedPublisher PD

Label-free Medical Image Quality Evaluation by Semantics-aware Contrastive Learning in IoMT

ACKNOWLEDGMENT For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.Peer reviewedPostprin

A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles

Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

Acknowledgments This work was supported by the Dalian University Research Platform Project Funding: Dalian Wise Information Technology of Med and Health Key Laboratory, the National Natural Science Foundation of China: Research on the stability of multi-surface high-speed unmanned boat formation and the method of cooperative collision avoidance in complex sea conditions, NO.61673084.Peer reviewedPostprintPublisher PD

Fine-grained RNN with Transfer Learning for Energy Consumption Estimation on EVs

This work is supported by the Engineering and Physical Sciences Research Council, under PETRAS SRF grant MAGIC (EP/S035362/1) and the University of Glasgow Impact Acceleration Account.Peer reviewedPostprin

Domain-adapted driving scene understanding with uncertainty-aware and diversified generative adversarial networks

Funding Information: This work was supported by Fisheries Innovation & Sustainability (FIS) and the U.K. Department for Environment, Food & Rural Affairs (DEFRA) under grant number FIS039 and FIS045A.Peer reviewedPublisher PD

A machine learning based personalized system for driving state recognition

Reliable driving state recognition (e.g. normal, drowsy, and aggressive) plays a significant role in improving road safety, driving experience and fuel efficiency. It lays the foundation for a number of advanced functions such as driver safety monitoring systems and adaptive driving assistance systems. In these applications, state recognition accuracy is of paramount importance to guarantee user acceptance. This paper is mainly focused on developing a personalized driving state recognition system by learning from non-intrusive, easily accessible vehicle related measurements and its validation using real-world driving data. Compared to conventional approaches, this paper first highlights the necessities of adopting a personalized system by analysing feature distribution of individual driver’s data and all drivers’ data via advanced data visualization and statistical analysis. If significant differences are identified, a dedicated personalized model is learnt to predict the driver’s driving state. Spearman distance is also drawn to evaluate the differences between individual driver’s data and all drivers’ data in a quantitative manner. In addition, five categories of classifiers are tested and compared to identify a suitable one for classification, where random forest with Bayesian parameter optimization outperforms others and therefore is adopted in this paper. A recently collected dataset from real-world driving experiments is adopted to evaluate the proposed system. Comparative experimental results indicate that the personalized learning system with road information significantly outperforms conventional approaches without considering personalized characteristics or road information, where the overall accuracy increases from 81.3% to 91.6%. It is believed that the newly developed personalized learning system can find a wide range of applications where diverse behaviours exist