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Citation Information : Transport Problems. Volume 15, Issue 4, Part 1, Pages 83-94, DOI: https://doi.org/10.21307/tp-2020-050
License : (CC BY 4.0)
Received Date : 13-May-2019 / Accepted: 27-November-2020 / Published Online: 31-December-2020
Ensuring the effectiveness of adaptive algorithms for advanced driver assistance systems (ADAS) requires online recognition of driving styles. The article discusses studies carried out during real driving cycles based on the GPS parameters and OBD system data of a hybrid vehicle. The work focuses on the search for measures of the speed and acceleration signals of the car and the measures determined on their basis that best describe the driving style responsible for the vehicle traffic safety and ecological safety. Relations between the type of driver, driving dynamics, and fuel consumption were studied. The driver's categorization was based on a statistical analysis of input signals and mean tractive force (MTF) by clustering.
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