Convolutional Neural Network for Driving Maneuver Identification Based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS)

Abstract

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Identification and translation of different driving maneuver are some of the key elements to analysis driving risky behavior. However, the major obstacles to maneuver identification are the wide variety of styles of driving maneuver which are performed during driving. The objective in this contribution through the paper is to automatic identification of driver maneuver e.g., driving in roundabouts, left and right turns, breaks, etc. based on Inertia Measurement Unit (IMU) and Global Positioning System (GPS). Here, several machine learning (ML) algorithms i.e., Artificial Neural Network (ANN), Convolutional Neural Network (CNN), K-nearest neighbor (k-NN), Hidden Markov Model (HMM), Random Forest (RF), and Support Vector Machine (SVM) have been applied for automatic feature extraction and classification on the IMU and GPS data sets collected through a Naturalistic Driving Studies (NDS) under an H2020 project called SimuSafe1. The CNN is further compared with HMM, RF, ANN, k-NN and SVM to observe the ability to identify a car maneuver through roundabouts. According to the results, CNN outperforms (i.e., average F1-score of 0.88 both roundabout and not roundabout) among the other ML classifiers and RF presents better correlation than CNN, i.e., MCC = −0.022.

Keywords

• convolutional neural network
• driving maneuver
• inertial measurement unit
• global positioning system
• machine learning