An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment

Abstract

Accurately acquiring the ecolevel of individual driver performance is the precondition for more targeted ecodriving behavior optimization. Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. Based on driving simulator tests, data of driver performance and vehicle fuel consumption were collected. The ecolevel was indicated as the ecoscore corresponding to vehicle fuel consumption. The model input was designed as 10 feature indexes of driver performance (e.g., percentage number, mean value, standard deviation, and power of applying acceleration pedal). The output was treated as ecoscore. Taking a number of one hundred of data segments in vehicle starting process as training sample, the optimal structure, functions, and learning rate of a backpropagation neural network model with three layers were obtained, after repeated model simulation experiments. The validation test of 16 sample data items showed that the mean prediction accuracy of our developed model was 92.89%. In addition, comparative analysis displayed that the performance of backpropagation neural network based model was better than linear regression based model and random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the ecolevel of driver performance. The study results provide an effective method to grasp the ecolevel of driver performance and further contribute to driving behavior optimization towards vehicle fuel consumption and emissions reduction.

Development of a Practical Method to Estimate the Eco-Level of Driver Performance

“…Since machine learning has advantage in mining hidden and complicated features, it is effective to develop a machine learning model to estimate the eco-level of driver performance precisely. In one of our previous studies [44], we found that the performance of BP network based model was better than that of the random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the eco-level of driver performance. us, we developed a BP network based model to qualify drivers' performance in terms of fuel consumption.…”

“…Overall, this research proposed a new and practical method to estimate the eco-level of driver performance based on OBD + GPS data in naturalistic driving conditions. Combined with our previously developed model to estimate vehicle fuel consumption by driver manipulating data (e.g., controlling the steering wheel, accelerator pedal, and decelerator pedal) in driving simulator [44], it tested and verified that BP network based model did have an advantage and applicability in exploring the relationship between vehicle fuel consumption and driver behaviors, thus contributing to qualifying the eco-level of drivers' performance from the perspective of fuel consumption. Moreover, the difference between our previously developed model and the current proposed model was obvious, namely, in terms of the input parameters, node number of hidden layer, training function, and learning rate.…”

“…Since training functions would apparently affect mode training speed and might further influence the accuracy of predictive results, the most suitable training function used in this study was also confirmed from repeated experiment tests. Five common training functions were selected as candidates by referring to other similar models developed in previous researches [44].…”

“…e prediction error would be smaller at the end of iteration if setting the learning rate as smaller value; however, the model convergence rate would be slower accordingly because of increasing learning time. In general, the method of learning rate selection was based on previous studies and repeated trials [39][40][41][42][43][44]. Usually, the optimal learning rate should have the smallest sum of squared errors through comparative analysis.…”

Impact of connected vehicle environment on driving performance: A case of an extra‐long tunnel scenario

“…This study carried out this research using a driving simulator from Chinese drivers and driving environment perspective. Moreover, the validity of driving simulator used in this study in studying driving behaviour, traffic safety, and eco-driving has been established in previous research [34][35][36][37][38][39]. were used to build the simulation CV environment [41,42].…”