Abstract
Identification and translation of different driving manoeuvre are some of the key elements to analysis driving risky behavior. However, the major obstacles to manoeuvre identification are the wide variety of styles of driving manoeuvre which are performed during driving. The objective in this contribution through the paper is to automatic identification of driver manoeuvre 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 SimuSafe . The CNN is further compared with HMM, RF, ANN, k-NN and SVM to observe the ability to identify a car manoeuvre 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 = -.022.
Highlights
Since the number of vehicles and road traffic users are increasing rapidly, the number of accidents is increasing
The study determines Convolutional Neural Network (CNN) as a best machine learning (ML) algorithm to identify driving maneuver based on Inertia Measurement Unit (IMU) and Global Positioning System (GPS) data collected through the Naturalistic Driving Studies (NDS) in SimuSafe
In our previous work (Altarabichi et al, 2019) and (Larose, 2005), several ML algorithms such as Artificial Neural Network (ANN), Random Forest (RF), K-nearest neighbor (k-NN), Logistic Regression (LR), Support Vector Machine (SVM) have been applied in Inertial Measurement Unit (IMU) and 3-D sensors that had been mounted in a car and motorcycle to classify roundabouts, left turns, right turns, straights, and stops
Summary
Since the number of vehicles and road traffic users are increasing rapidly, the number of accidents is increasing. The European Commission is aiming to reduce the number of road accidents with the vision to achieve zero road fatalities in EU (KPMG, 2012; Fagnant and Kockelman, 2013) To meet this objective and to improve the traffic safety and efficiency, it is necessary to understand the wide variety of styles for driving maneuver which promotes initiatives to conduct researches to reduce the rate of road accidents. The main challenge in this study is to determine the best Machine Learning (ML) algorithm for identifying driving maneuver i.e., roundabouts from the NDS based on the sensory data from IMU and GPS by investigating several ML algorithms. The study determines Convolutional Neural Network (CNN) as a best ML algorithm to identify driving maneuver based on Inertia Measurement Unit (IMU) and Global Positioning System (GPS) data collected through the NDS in SimuSafe. The test vehicular sensors are used to capture different aspects of driving dynamics and motion to identify roundabout as a driving maneuver
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
