Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements
Abstract Eco‐driving assistance systems incorporating predictive or feedforward information are a promising technique to increase energy‐efficiency and reduce CO2 emissions from road transportation. This work gives details of such a system that was recently developed by the authors, which uses real‐...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12047 |
| _version_ | 1811266484123467776 |
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| author | James Fleming Xingda Yan Craig Allison Neville Stanton Roberto Lot |
| author_facet | James Fleming Xingda Yan Craig Allison Neville Stanton Roberto Lot |
| author_sort | James Fleming |
| collection | DOAJ |
| description | Abstract Eco‐driving assistance systems incorporating predictive or feedforward information are a promising technique to increase energy‐efficiency and reduce CO2 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 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, 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. |
| first_indexed | 2024-04-12T20:42:57Z |
| format | Article |
| id | doaj.art-c765c236568c45769cda8a96f8c78323 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-12T20:42:57Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-c765c236568c45769cda8a96f8c783232022-12-22T03:17:21ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-04-0115457358310.1049/itr2.12047Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurementsJames Fleming0Xingda Yan1Craig Allison2Neville Stanton3Roberto Lot4Wolfson School of Mechanical Electrical and Manufacturing Engineering Epinal Way Loughborough LE11 3TU UKCompound Semiconductor Applications Catapult Imperial Park Innovation Centre Celtic Way Newport NP10 8BE UKFaculty of Sport Health and Social Sciences Solent University East Park Terrace Southampton SO14 0YN UKFaculty of Engineering and Physical Sciences University of Southampton, University Rd, Highfield Southampton SO17 1BJ UKDepartment of Industrial Engineering University of Padova, via 8 Febbraio Padova 35122 ItalyAbstract Eco‐driving assistance systems incorporating predictive or feedforward information are a promising technique to increase energy‐efficiency and reduce CO2 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 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, 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.https://doi.org/10.1049/itr2.12047Optimisation techniquesEnergy conservationAutomobile electronics and electrics |
| spellingShingle | James Fleming Xingda Yan Craig Allison Neville Stanton Roberto Lot Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements IET Intelligent Transport Systems Optimisation techniques Energy conservation Automobile electronics and electrics |
| title | Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements |
| title_full | Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements |
| title_fullStr | Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements |
| title_full_unstemmed | Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements |
| title_short | Real‐time predictive eco‐driving assistance considering road geometry and long‐range radar measurements |
| title_sort | real time predictive eco driving assistance considering road geometry and long range radar measurements |
| topic | Optimisation techniques Energy conservation Automobile electronics and electrics |
| url | https://doi.org/10.1049/itr2.12047 |
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