M. E. Mumcuoglu, G. Alcan, M. Unel, O. Cicek, M. Mutluergil, M. Yilmaz, K. Koprubasi
46st Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), Singapore, October 18-21, 2020
Publication date: 2020


In this paper, we present a real-time driver evaluation system for heavy-duty vehicles by focusing on the classification of risky acceleration and braking behaviors. We utilize an improved version of our previous Long Short Term Memory (LSTM) based acceleration behavior model [10] to evaluate varying acceleration behaviors of a truck driver in small time periods. This model continuously classifies a driver as one of six driver classes with specified longitudinal-lateral aggression levels, using driving signals as time-series inputs. The driver gets acceleration score updates based on assigned classes and the geometry of driven road sections. To evaluate the braking behaviors of a truck driver, we propose a braking behavior model, which uses a novel approach to analyze deceleration patterns formed during brake operations. The braking score of a driver is updated for each brake event based on the pattern, magnitude, and frequency evaluations. The proposed driver evaluation system has achieved significant results in both the classification and evaluation of acceleration and braking behaviors.


  • Driver evaluation
  • Driver behaviors
  • Classification
  • LSTM networks
  • Heavy-duty vehicles
  • Acceleration
  • Braking


  title={Driver evaluation in heavy duty vehicles based on acceleration and braking behaviors},
  author={Mumcuoglu, Mehmet Emin and Alcan, Gokhan and Unel, Mustafa and Cicek, Onur and Mutluergil, Mehmet and Yilmaz, Metin and Koprubasi, Kerem},
  booktitle={IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society},