Eco-driving assistance system for a manual transmission bus based on machine learning
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 12 - Engineering
- Output identifier
- 17017120
- Type
- D - Journal article
- DOI
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10.1109/TITS.2017.2775633
- Title of journal
- IEEE Transactions on Intelligent Transportation Systems
- Article number
- -
- First page
- 572
- Volume
- 19
- Issue
- 2
- ISSN
- 1524-9050
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work collaborates with China's largest independent engine manufacturer, Guangxi Yuchai Group Co., Ltd., to improve diesel engines' fuel economy in practical applications. This paper is important because it is the industry’s first data-driven self-learning driver assistance system instead of a traditional rule-based system. This technology is expected to improve the engine fuel economy of commercial vehicle by 3%. (Dr Zhibo Ban, banzhibo@yuchai.cn, Director of Advanced Technology Centre).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -