https://scholars.lib.ntu.edu.tw/handle/123456789/432301
標題: | Behavioral classification of drivers for driving efficiency related ADAS using artificial neural network | 作者: | Cheng Z.-J. Jeng L.-W. Li K. KANG LI |
關鍵字: | ADAS; ANN; Behavior classifcation; Efficiency | 公開日期: | 2019 | 出版社: | Institute of Electrical and Electronics Engineers Inc. | 起(迄)頁: | 173-176 | 來源出版物: | 2018 IEEE International Conference on Advanced Manufacturing | 摘要: | The driver states, driving styles and aggressiveness strongly influences vehicle control, and energy efficiency. If the driving patterns can be collected and effectively analyzed the resulting classification can greatly improve the effectiveness and design of active safety system, advanced driving assistance system (ADAS) or energy efficient control. For an efficiency oriented analysis, artificial neural network (ANN) is used to classify drivers into aggressive, normal, and calm states through three different driving inputs: vehicle acceleration, speed and throttle pedal angle. The resultant models have fairly accurate classification according to different driving scenarios, with overall accuracy of 90%. The classification can be a reminder for the drivers of their current behavior, in-order for the drivers to take necessary actions to improve the driving condition. © 2018 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062240956&doi=10.1109%2fAMCON.2018.8614836&partnerID=40&md5=e8c78051d039e3840d34068afa723d6f https://scholars.lib.ntu.edu.tw/handle/123456789/432301 |
ISBN: | 9781538656099 | DOI: | 10.1109/AMCON.2018.8614836 | SDG/關鍵字: | Active safety systems; Behavioral research; Control system synthesis; Efficiency; Energy efficiency; Manufacture; Neural networks; ADAS; Classifcation; Current behaviors; Driving assistance systems; Driving conditions; Energy efficient; Overall accuracies; Vehicle acceleration; Advanced driver assistance systems |
顯示於: | 機械工程學系 |
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