Detection of Evasive Maneuvers for Surrounding Vehicles
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Oleszko, Sebastian
Wölfinger, Alexander
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Advanced Driver-Assistance Systems is a great aid to avoid traffic accidents, but
today it relies on sensor sight. An evasive maneuver from a target vehicle is an
example of a scenario where these systems have limitations. This thesis investigates
the possibility to classify an evasive maneuver using a machine learning approach
with a limited test-track dataset combined with a real-world dataset. Time series
classification and data augmentation are used for different machine learning models
for comparison.
The result shows that the models are capable of predicting evasive maneuvers, however,
it is clear by these results that further work to advance these models should be
made. Data augmentation for time series data showed promising results in increasing
the variety of input data and therefore increasing the limited dataset.
A conclusion is made that a search algorithm that finds evasive maneuvers in larger
datasets can be developed using the methods that are implemented in this thesis.
Thus reducing data limitations that were an issue in this thesis. Future work could
expand upon the models to include more complex features such as traffic interaction.
Beskrivning
Ämne/nyckelord
ADAS, Evasive maneuver, Time series classification, Data augmentation