A natural language processing approach for identifying driving styles in curves

Visa enkel post
dc.contributor.authorMcNabb, Eric
dc.contributor.authorKalander, Marcus
dc.contributor.departmentChalmers tekniska högskola / Institutionen för tillämpad mekaniksv
dc.contributor.departmentChalmers University of Technology / Department of Applied Mechanicsen
dc.date.accessioned2019-07-03T14:16:23Z
dc.date.available2019-07-03T14:16:23Z
dc.date.issued2016
dc.description.abstractA machine able to autonomously recognise driving styles has numerous applications, of which the most straightforward is to recognise risky behaviour. Such knowledge can be used to teach new drivers with the goal of reducing accidents in the future and increasing traffic safety for all road users. Furthermore, insurance companies can incentivise safe driving with lower premiums, which in turn can motivate a more careful driving style. Another application is within the field of autonomous vehicles where learning about driving styles is imperative for autonomous vehicles to be able to interact with other drivers in traffic. The first step towards identifying different driving styles is being able to recognise and distinguish between them. The aim of this thesis is to identify the indicators of aggressive driving in curves from a large amount of naturalistic driving data. The first step was finding curve sections to analyse within trips and the second step was reducing the data to become more manageable. Symbolic representations were used for the second preprocessing step, which in turn allowed the use of Natural Language Processing techniques for the analysis. We categorise drivers into different groups depending on their perceived tendency towards aggressive driving styles. This categorisation is used to compare the drivers and their driving style with each other. The tendencies used were Speeding, Braking, Jerky curve handling and Rough curve handling. Some general trends among the analysed drivers are also identified. It is possible to reuse the categorisation to include more drivers in the future or to use what we have learned about the features and drivers for further research.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/239085
dc.language.isoeng
dc.relation.ispartofseriesDiploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden : 2016:05
dc.setspec.uppsokTechnology
dc.subjectInformations- och kommunikationsteknik
dc.subjectTransport
dc.subjectHållbar utveckling
dc.subjectFarkostteknik
dc.subjectStrömningsmekanik och akustik
dc.subjectInformation & Communication Technology
dc.subjectTransport
dc.subjectSustainable Development
dc.subjectVehicle Engineering
dc.subjectFluid Mechanics and Acoustics
dc.titleA natural language processing approach for identifying driving styles in curves
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeComputer science – algorithms, languages and logic (MPALG), MSc
Ladda ner
Original bundle
Visar 1 - 1 av 1
Bild (thumbnail)
Namn:
239085.pdf
Storlek:
7.8 MB
Format:
Adobe Portable Document Format
Beskrivning:
Fulltext
Ladda ner
Samling
Examensarbeten för masterexamen