Real-time predictive eco-driving assistance considering road geometry and long-range radar measurements

James Fleming, Xingda Yan, Craig Allison, Neville Stanton, Roberto Lot

Research output: Contribution to journalArticlepeer-review


Eco-driving assistance systems incorporating predictive or feedforward information are a promising technique to
increase energy-efficiency and reduce CO2 emissions from road transportation. In this work, we give details of such a system that was recently developed by the authors, which uses real-time data from GPS and automotive radar to perform a predictive optimisation of a vehicle’s speed profile and coaches a driver into fuel-saving and CO2-reducing behaviour. A repeated-measures study carried out in a fixed-base driving simulator indicated an overall reduction in fuel consumption of 6.09%, which was significantly greater than improvements expected from reductions in average speed. Adjusted for average speed, fuel-efficiency improvements when using the system are similar to those observed in unassisted eco-driving, but with improvements in travel time in motorway situations. Finally, we describe an on-road prototype in which the optimisation is solved using data from vehicle sensors, successfully demonstrating that real-time implementation of the system is feasible.
Original languageEnglish
JournalIET Intelligent Transport Systems
Publication statusAccepted/In press - 3 Feb 2021

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