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

Ahmed, Mobyen Uddin, Dr, 1976- (author): Mälardalens högskola,Inbyggda system

(creator_code:org_t)

2020-07-24
2020
English.
In: Frontiers in Sustainable Cities. - : Frontiers Media SA. - 2624-9634. ; 2

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Abstract Subject headings

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.

TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Computer Systems

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