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

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.

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

“…The measurement of driver's biological data is of great help to improve the safety of autonomous vehicles and the ability of vehicles to avoid emergencies. A. Seckin et al [ 222 ] designed a driver data collection and marking system using EEG, EMG, and IMU in a comprehensive way and extracted features for training to improve the vehicle's ability to identify dangerous situations in autonomous driving. The data are classified by the KNN algorithm after PCA dimensionality reduction.…”

Sürüş Davranış Tespiti ve Tasnifi için Takograflara Yönelik Yeni Bir Algoritma Tasarımı ve Uygulaması

“…Otonom sürüş çalışmalarında nesnelerin ve yol çizgilerinin algılanmasında genellikle kamera görüntüleri ve LIDAR dönüşleri kullanılmış, görüntü işleme tekniklerinden istifade edilmiştir [27,28]. Sürücü davranışlarına odaklanan çalışmaların bir kısmında biyomedikal sensörler ve görüntü işleme teknikleri de kullanılmıştır [29][30][31]. Görüntü işleme tekniklerinin yüksek işlem ve hafıza kapasiteli mikrodenetleyiciler veya bilgisayarlar gerektirmesi maliyeti artırmaktadır.…”