Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines

Abstract

The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correlation analysis. Using a k-means clustering algorithm, driving style can be divided into three types: normal type, soft type, and aggressive type, and we verified the validity of this driving style classification with a box plot. On this basis, the parameters of random forest, k-nearest neighbor, support vector machine, and neural network models were optimized and the accuracy was compared through a cross-validation grid search, and then a driving style identification model based on the random forest method was finally proposed. Driving style parameter weight values were obtained based on the Gini coefficient. Last, the fuel consumption characteristics of different driving styles were calculated. The results show that the driving style identification models based on random forest can effectively identify different driving styles when the mining truck is operating under heavy load and no load, and the overall accuracy of the model is 95.39% and 90.74% respectively. The fuel consumption of the aggressive driving style was the largest and was 10% higher than the average fuel consumption. The research results provide data support and new ideas for operation training and fuel-saving driving of mining trucks in open-pit mines.

Enhanced Fuel Efficiency via Eco-Driving Training in Freight Transport

“…In the study [39], it was found that the fuelsaving driving skills of truck drivers are a significant factor in reducing fuel usage. And according to Wang et al [40], the fuel consumption of aggressive driving styles affects the average fuel consumption by 10%.…”

Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

“…In addition, DILISP can collect the sport appearance of two vehicles and record the drivers actions. Qun Wang et al [3] designed a data acquisition system based on an inertial navigation sensor and advanced RISC machine microcontroller that can collect the driver's driving behavior and vehicle status in real time. Derick A. Johnson et al [4] collected the speed and direction data of the vehicle during driving using the rear camera, accelerometer, gyroscope, and GPS of a smartphone.…”

Analysis of driving style using self-organizing maps to analyze driver behavior

“…CNN, long short-term memory (LSTM) network, and pre train-LSTM [15], [17], [20], random forest [16] - [14], [17], [20]/+ [15], [16] -+ + Application of self-organizing maps [21] + + Classification method, which is enabled by ensemble learning [23] --+ +…”