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Road Surface Preview Estimation Using a Monocular Camera
Linköping University, Department of Electrical Engineering, Computer Vision.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recently, sensors such as radars and cameras have been widely used in automotives, especially in Advanced Driver-Assistance Systems (ADAS), to collect information about the vehicle's surroundings. Stereo cameras are very popular as they could be used passively to construct a 3D representation of the scene in front of the car. This allowed the development of several ADAS algorithms that need 3D information to perform their tasks. One interesting application is Road Surface Preview (RSP) where the task is to estimate the road height along the future path of the vehicle. An active suspension control unit can then use this information to regulate the suspension, improving driving comfort, extending the durabilitiy of the vehicle and warning the driver about potential risks on the road surface. Stereo cameras have been successfully used in RSP and have demonstrated very good performance. However, the main disadvantages of stereo cameras are their high production cost and high power consumption. This limits installing several ADAS features in economy-class vehicles. A less expensive alternative are monocular cameras which have a significantly lower cost and power consumption. Therefore, this thesis investigates the possibility of solving the Road Surface Preview task using a monocular camera. We try two different approaches: structure-from-motion and Convolutional Neural Networks.The proposed methods are evaluated against the stereo-based system. Experiments show that both structure-from-motion and CNNs have a good potential for solving the problem, but they are not yet reliable enough to be a complete solution to the RSP task and be used in an active suspension control unit.

Place, publisher, year, edition, pages
2018. , p. 85
Keywords [en]
Road Surface Preview, Computer Vision, Depth Estimation, Convolutional Neural Network, CNN, traffic safety, monocular camera, mono vision system, mono camera, Structure from motion, sfm, 3D Reconstruction, Autonomous Driving
Keywords [sv]
Datorseende, trafiksäkerhet, djupuppskattning, mono kamera, 3D rekonstruktion, autonoma fordon
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-151873ISRN: LiTH-ISY-EX--18/5173--SEOAI: oai:DiVA.org:liu-151873DiVA, id: diva2:1253882
External cooperation
Veoneer
Subject / course
Computer Vision Laboratory
Presentation
2018-10-02, Systemet, Linköpings Universitet, Linköping, 15:30 (English)
Supervisors
Examiners
Available from: 2018-10-08 Created: 2018-10-07 Last updated: 2018-10-08Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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