Yıl 2019, Cilt 12 , Sayı 4, Sayfalar 299 - 305 2019-10-29

Otonom Araçların Görsel Eğitimi için EEG, EMG ve IMU ile Etiketleme Sistemi
Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles

Ahmet Çağdaş SEÇKİN [1]


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.

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.

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Birincil Dil en
Konular Bilgisayar Bilimleri, Bilgi Sistemleri
Bölüm Makaleler
Yazarlar

Orcid: 0000-0002-9849-3338
Yazar: Ahmet Çağdaş SEÇKİN (Sorumlu Yazar)
Kurum: UŞAK ÜNİVERSİTESİ
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 29 Ekim 2019

Bibtex @araştırma makalesi { gazibtd542662, journal = {Bilişim Teknolojileri Dergisi}, issn = {1307-9697}, eissn = {2147-0715}, address = {}, publisher = {Gazi Üniversitesi}, year = {2019}, volume = {12}, pages = {299 - 305}, doi = {10.17671/gazibtd.542662}, title = {Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles}, key = {cite}, author = {SEÇKİN, Ahmet Çağdaş} }
APA SEÇKİN, A . (2019). Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles. Bilişim Teknolojileri Dergisi , 12 (4) , 299-305 . DOI: 10.17671/gazibtd.542662
MLA SEÇKİN, A . "Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles". Bilişim Teknolojileri Dergisi 12 (2019 ): 299-305 <https://dergipark.org.tr/tr/pub/gazibtd/issue/49914/542662>
Chicago SEÇKİN, A . "Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles". Bilişim Teknolojileri Dergisi 12 (2019 ): 299-305
RIS TY - JOUR T1 - Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles AU - Ahmet Çağdaş SEÇKİN Y1 - 2019 PY - 2019 N1 - doi: 10.17671/gazibtd.542662 DO - 10.17671/gazibtd.542662 T2 - Bilişim Teknolojileri Dergisi JF - Journal JO - JOR SP - 299 EP - 305 VL - 12 IS - 4 SN - 1307-9697-2147-0715 M3 - doi: 10.17671/gazibtd.542662 UR - https://doi.org/10.17671/gazibtd.542662 Y2 - 2019 ER -
EndNote %0 Bilişim Teknolojileri Dergisi Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles %A Ahmet Çağdaş SEÇKİN %T Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles %D 2019 %J Bilişim Teknolojileri Dergisi %P 1307-9697-2147-0715 %V 12 %N 4 %R doi: 10.17671/gazibtd.542662 %U 10.17671/gazibtd.542662
ISNAD SEÇKİN, Ahmet Çağdaş . "Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles". Bilişim Teknolojileri Dergisi 12 / 4 (Ekim 2019): 299-305 . https://doi.org/10.17671/gazibtd.542662
AMA SEÇKİN A . Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles. Bilişim Teknolojileri Dergisi. 2019; 12(4): 299-305.
Vancouver SEÇKİN A . Labeling System with EEG, EMG, and IMU for Visual Training of Autonomous Vehicles. Bilişim Teknolojileri Dergisi. 2019; 12(4): 305-299.