Otonom Araçların Görsel Eğitimi için EEG, EMG ve IMU ile Etiketleme Sistemi

Year 2019, , 299 - 305, 29.10.2019

Ahmet Çağdaş Seçkin

https://doi.org/10.17671/gazibtd.542662

Cited By: 3

Abstract

Otonom araçlar, çevrelerini algılayarak kararlar alan ve bu kararlar ile hareket eden araçlardır. Günümüzde otonom araçlar bazı ülkelerde trafikte de kullanılmaktadır. Otonom araçlarda çevre algılama için çeşitli kameralar, lazer radarlar (LIDAR), sonar sensörler vb. pek çok sensör kullanılmaktadır. Çevre algılandıktan sonra toplanan veri makine öğrenmesi yöntemleri yardımıyla araca öğretilmekte ve araç trafik kurallarına uyarak hedefe ulaşmaktadır. Trafik kuralları noktasında en büyük görev görüntü tabanlı sistemlere düşmektedir. Ancak ideal trafik koşulları ve çevre şartları her zaman sağlanamamaktadır. Bu nedenle otonom araçlar için tehlike oluşturabilecek durumların tespiti önem arz etmektedir. Literatür incelendiğinde tehlikeli durumların etiketli bulunduğu görsel bir veri seti veya ilgili bir bilimsel çalışmaya rastlanmamıştır. Bu çalışmada literatürdeki açığı gidermek için bir veri toplama ve etiketleme sistemi tasarlanması amaçlanmıştır. Amaç doğrultusunda tasarlanan sistemde insan sürüşü esnasında sürücünün fizyolojik verisi (EEG ve EMG) ve eylemsizlik değişim verilerinden otomatik olarak video etiketi oluşturan bir sistem tasarlanmıştır. Bunun için öncelikle deneyler ile sensör sinyalleri toplanmıştır. Toplanan sinyallerden 0.33 sn uzunluğunda üst üste binmeyen kayan pencere kullanılarak zaman ve frekans alanında öznitelikler çıkarılmıştır. Elde edilen veri setindeki giriş değişkenleri Temel Bileşen Analizi (PCA) ile indirgenmiş ve Karar Ağacı (DT), Rastgele Ağaç (RF) ve K En Yakın Komşular (K-NN) algoritmaları ile sınıflandırma işlemine tutulmuştur. Bulgulara göre K-NN yönteminin 0.922 doğrulukla tehlikeli, tehlikesiz durumları ayırt ederek denenen algoritmalar arasında en başarılı algoritma olduğu tespit edilmiştir.

Keywords

EEG, EMG, IMU, otonom araçlar, insan makine etkileşimi, makine öğrenmesi

Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles

Abstract

Autonomous vehicles are tools that make decisions and take decisions by perceiving their environment. Today, autonomous vehicles are also used in traffic in some countries. Various types of cameras, laser radars (LIDAR), sonar distance sensors, etc. are used for environmental detection in autonomous vehicles. After the environment is perceived, the collected data is taught to the vehicle with the help of machine learning methods and the vehicle reaches the target by following the traffic rules. At the point of traffic rules, the biggest task belongs to image-based systems. However, ideal traffic conditions and environmental conditions are not always provided. It is important to identify situations that may present a danger to autonomous vehicles. When the literature is examined, no visual data set or a scientific study with dangerous labeling has been found. In this study, it is aimed to design a data collection and labeling system to overcome this gap in the literature. In the system designed for the purpose, a system which automatically creates a video label from the physiological data of the driver (EEG ve EMG) and the inertia change data during human driving is designed. For this reason, firstly, the sensor signals were collected by experiments. In the time and frequency field, attributes were extracted by using the non-overlapping sliding window with 0.33 sec length. The input variables in the data set were reduced by PCA and classified by DT, RF and K-NN algorithms. According to the preliminary study findings, the K-NN method was the most successful algorithm among the algorithms tested with 0.922 accuracy.

Keywords

EEG, EMG, IMU, autonomous vehicles, human-machine interaction, machine learning

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