Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning controller for prediction of lane change times: A study of driving behaviour using naive Bayes and Artificial Neural Networks
2017 (English)Independent thesis Advanced level (professional degree), 300 HE creditsStudent thesis
Abstract [en]

Today's trucks are becoming more and more safe due to the use of an Advanced Driver Assistance System (ADAS). This system is aimed to assist the driver in the driving process, and to increase the safety for both the driver and the environment around the vehicle. These systems require strict design criteria to enable sufficiently high precision and robustness. ADAS are developing intensely today, and these systems represent a way towards a completely autonomous vehicle community. The main focus of this master thesis project is to investigate the possibility of predicting a driver's typical lane change time before the truck reaches a highway. This was done by trying to identify the driving behaviour using sensor data from non-highway driving. Techniques from machine learning, such as naive Bayes and Artificial Neural Networks (ANN), with various combinations of sensor inputs were used during this process. The results indicate that the assumption that different driving behaviours are representing different lane change times is true. Furthermore, predicting lane change times in whole seconds was as difficult as predicting lane change of three classes, fast, medium and slow. Predicting fast or slow lane change gave a better result. Only one set of validation data of totally five was predicted incorrectly. There was no big difference in the results between naive Bayes and the designed ANN. However, the results were not good enough for practical use, and more research is needed. Methods for increasing the performance and future work are also discussed.

Place, publisher, year, edition, pages
2017. , 30 p.
Series
UPTEC E, ISSN 1654-7616 ; 17 001
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-322983OAI: oai:DiVA.org:uu-322983DiVA: diva2:1104666
External cooperation
Scania CV AB
Educational program
Master Programme in Electrical Engineering
Supervisors
Examiners
Available from: 2017-06-02 Created: 2017-06-01 Last updated: 2017-06-02Bibliographically approved

Open Access in DiVA

fulltext(1002 kB)50 downloads
File information
File name FULLTEXT01.pdfFile size 1002 kBChecksum SHA-512
72ff1412f46fca036c8137767937df186dc5b6ae89463950367c03d5aca83fe42e201a6434766a1bcf44dfd44d71b6a3ab490e09f55e16aa5018ef7ce3262891
Type fulltextMimetype application/pdf

Electrical Engineering, Electronic Engineering, Information EngineeringEngineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 50 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 303 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf