Open AccessReal‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements
Author(s) -
Fleming James,
Yan Xingda,
Allison Craig,
Stanton Neville,
Lot Roberto
Publication year - 2021
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/itr2.12047
Subject(s) - fuel efficiency , automotive engineering , range (aeronautics) , global positioning system , radar , automotive industry , work (physics) , intelligent transportation system , simulation , advanced driver assistance systems , engineering , computer science , transport engineering , artificial intelligence , mechanical engineering , telecommunications , aerospace engineering
Eco‐driving assistance systems incorporating predictive or feedforward information are a promising technique to increase energy‐efficiency and reduce CO 2 emissions from road transportation. This work gives 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 CO 2 ‐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, an on‐road prototype is described in which the optimisation is solved using data from vehicle sensors, successfully demonstrating that real‐time implementation of the system is feasible.