Detection of secondary task engagement in naturalistic driving data
Typ
Examensarbete för masterexamen
Program
Automotive engineering (MPAUT), MSc
Publicerad
2020
Författare
Hulukunte Gopinath, Sriranga
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Fatalities related to road traffic accidents are up to 25,000 in the EU annually. In most
cases, these accidents occur due to human error in judgement or action. Driving requires
undivided attention, but studies show that drivers often engage in secondary tasks which
result in distraction causing accidents. To analyse this behaviour of drivers, naturalistic
driving studies provide video data of drivers indulging in this behaviour.
Meanwhile, progress in computer vision and machine learning has led to algorithms capable
of automatically detecting objects in images or videos. Convolutional neural networks
(CNNs) are the most common artificial neural network used for such applications. This
thesis work focuses on using the latest object detection algorithm named YOLO (You
Only Look Once) to detect secondary tasks in images from naturalistic driving data. The
algorithm is capable of detecting custom objects provided it is trained for them. The
distractions caused due to engagement in secondary tasks were categorised and manually
labelled. The data was categorised into 9 types of distractions. The labelled data was
trained on a cloud virtual machine.
The results were noted for three different trials, and each trial varied in data size and
classes of secondary tasks trained. Trial 3 had the best results with an average detection
rate per class of around 88%. This trial was most comprehensive, and it was an iterated
improvement based on the observations from previous trials. To make the algorithm
robust, it needs to be trained with different datasets to arrive at a generalised model. This
work aims to reduce the effort of manually annotating secondary tasks in huge naturalistic
driving data.
Beskrivning
Ämne/nyckelord
Secondary tasks , CNN , YOLO