This book discusses an integrative approach combining Human Factors expertise with Automotive Engineering. It develops an in-depth case study of designing a fuel-efficient driving intervention and offers an examination of an innovative study of feed-forward eco-driving advice.

Assisted Eco-Driving: A Practical Guide to the Design and Testing of an Eco-Driving Assistance System offers an examination of an innovative study of feed-forward eco-driving advice based on current vehicle and road environment status. It presents lessons, insights and utilises a documented scientific and research-led approach to designing novel speed advisory and fuel use minimisation systems suitable for combustion vehicles, hybrids and electric vehicles

The audience consists of system designers and those working with interfaces and interactions, UX, human factors and ergonomics and system engineering. Automotive academics, researchers, and practitioners will also find this book of interest.

About the Author

Dr. Craig K. Allison earned his PhD in Web Science (Psychology) from the University of Southampton in 2016. Craig received his M.Sc in Web Science from the University of Southampton in 2011, and his B.Sc in Psychology in 2009, also from the University of Southampton. Craig’s research background originated within spatial psychology, before transitioning to Human Factors research. Craig has worked on numerous topics, primarily related to the aviation and automotive industries. With expertise in both qualitative and quantitative analysis, Craig has extensive experience running research trials and working in multidisciplinary teams. Craig’s currently Lecturer in Psychology at Solent University, Southampton.

Dr. James M. Fleming earned the MEng and DPhil degrees in Engineering Science from the University of Oxford in 2012 and 2016 respectively, following which he spent three years as a Research Fellow at the University of Southampton before joining the Wolfson School of Mechanical, Electrical and Manufacturing Engineering at Loughborough University in September 2019. He has research interests in the theory and practice of optimal control and model predictive control, with applications to fuel- and energy- efficient driving, motorcycle stability and renewable energy.

Dr Xingda Yan earned the B.Eng. degree in automation from the Harbin Institute of Technology, Harbin, China, in 2012, and the Ph.D. degree in electrical engineering from the University of Southampton, Southampton, U.K., in 2017. He was a Research Fellow with the Mechanical Engineering Department at the University of Southampton. Xindga is currently an automotive power engineer at Compound Semiconductor Applications Catapult, Newport, UK and a visiting researcher with the Mechatronics Engineering Group, University of Southampton. His research interests include power electronics, hybrid system modelling and control, model predictive control, hybrid electric vehicle modelling, and energy management.

Dr Roberto Lot was Professor of Automotive Engineering at the University of Southampton (UK) from 2014 to August 2019 and has recently moved to the University of Padova (Italy). He earned a PhD in Mechanics of Machines in 1998 and a Master Degree cum laude in Mechanical Engineering in 1994 from the University of Padua (Italy). His research interests include both road and race vehicles, in particular dynamics and control. He has directed several national and international research projects and published more than 100 scientific papers and contributing to make our vehicles safer, faster, and more eco-friendly.

Professor Neville A. Stanton PhD, DSc, is a Chartered Psychologist, Chartered Ergonomist and Chartered Engineer. He holds the chair in Human Factors Engineering in the Faculty of Engineering and the Environment at the University of Southampton in the UK. He has degrees in Occupational Psychology, Applied Psychology and Human Factors Engineering and has worked at the Universities of Aston, Brunel, Cornell and MIT. His research interests include modelling, predicting, analysing and evaluating human performance in systems as well as designing the interfaces and interaction between humans and technology.

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