Driver Evaluation in Heavy Duty Vehicles Based on Acceleration and Braking Behaviors

Abstract

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 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.

Research On Driving Behavior Recognition By Smart Car Sensors

“…And achieved high accuracy. Mumcuoglu et al [14] utilized LSTM networks to evaluate the acceleration and braking behavior of heavy-duty vehicle drivers. Saleh et al [15] built a stacked LSTM neural network model using nine different sensor data captured by the internal sensors of the smartphone to classify normal driving, aggressive driving or drowsy driving, and the final model has an F1 score of 91%.…”