Detection of Evasive Maneuvers for Surrounding Vehicles

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dc.contributor.authorOleszko, Sebastian
dc.contributor.authorWölfinger, Alexander
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerSärkkä, Aila
dc.contributor.supervisorLilja, Adam
dc.contributor.supervisorKonsatantinou, Konstantinos
dc.date.accessioned2021-08-26T08:39:27Z
dc.date.available2021-08-26T08:39:27Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractAdvanced 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.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/303999
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectADAS, Evasive maneuver, Time series classification, Data augmentationsv
dc.titleDetection of Evasive Maneuvers for Surrounding Vehiclessv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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