FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3208911061> ?p ?o ?g. }

• W3208911061 abstract "During this thesis, some perception approaches for self-driving vehicles were developed using de convolutional neural networks applied to monocular camera images and High-Definition map (HD-ma rasterized images. We focused on camera-only solutions instead of leveraging sensor fusion with rang sensors because cameras are the most cost-effective and discrete sensors. The objective was also to show th camera-based approaches can perform at par with LiDAR-based solutions on certain 3D vision tasks. Rea world data was used for training and evaluation of the developed approaches but simulation was als leveraged when annotated data was lacking or for safety reasons when evaluating driving capabilities. Cameras provide visual information in a projective space where the perspective effect does not preserve th distances homogeneity. Scene understanding tasks such as semantic segmentation are then often operated i the camera-view space and then projected to 3D using a precise depth sensor such as a LiDAR. Having thi scene understanding in the 3D space is useful because the vehicles evolve in the 3D world and the navigatio algorithms reason in this space. Our focus was then to leverage the geometric knowledge about the camer parameters and its position in the 3D world to develop an approach that allows scene understanding in the 3D space using only a monocular image as input. Neural networks have also proven to be useful for more than just perception and are more and more used fo the navigation and planning tasks that build on the perception outputs. Being able to output 3D scen understanding information from a monocular camera has also allowed us to explore the possibility of havin an end-to-end holistic neural network that takes a camera image as input, extracts intermediate semantic information in the 3D space and then lans the vehicle's trajectory." @default.
• W3208911061 created "2021-11-08" @default.
• W3208911061 creator A5007357194 @default.
• W3208911061 date "2021-05-05" @default.
• W3208911061 modified "2023-09-23" @default.
• W3208911061 title "Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles" @default.
• W3208911061 hasPublicationYear "2021" @default.
• W3208911061 type Work @default.
• W3208911061 sameAs 3208911061 @default.
• W3208911061 citedByCount "0" @default.
• W3208911061 crossrefType "dissertation" @default.
• W3208911061 hasAuthorship W3208911061A5007357194 @default.
• W3208911061 hasConcept C153083717 @default.
• W3208911061 hasConcept C154945302 @default.
• W3208911061 hasConcept C31972630 @default.
• W3208911061 hasConcept C41008148 @default.
• W3208911061 hasConcept C65909025 @default.
• W3208911061 hasConcept C81363708 @default.
• W3208911061 hasConcept C89600930 @default.
• W3208911061 hasConceptScore W3208911061C153083717 @default.
• W3208911061 hasConceptScore W3208911061C154945302 @default.
• W3208911061 hasConceptScore W3208911061C31972630 @default.
• W3208911061 hasConceptScore W3208911061C41008148 @default.
• W3208911061 hasConceptScore W3208911061C65909025 @default.
• W3208911061 hasConceptScore W3208911061C81363708 @default.
• W3208911061 hasConceptScore W3208911061C89600930 @default.
• W3208911061 hasLocation W32089110611 @default.
• W3208911061 hasOpenAccess W3208911061 @default.
• W3208911061 hasPrimaryLocation W32089110611 @default.
• W3208911061 hasRelatedWork W1970387420 @default.
• W3208911061 hasRelatedWork W1973800854 @default.
• W3208911061 hasRelatedWork W1986984265 @default.
• W3208911061 hasRelatedWork W1993526413 @default.
• W3208911061 hasRelatedWork W2003053198 @default.
• W3208911061 hasRelatedWork W2018861445 @default.
• W3208911061 hasRelatedWork W2022934847 @default.
• W3208911061 hasRelatedWork W2036698173 @default.
• W3208911061 hasRelatedWork W2038594208 @default.
• W3208911061 hasRelatedWork W2088478889 @default.
• W3208911061 hasRelatedWork W2120322874 @default.
• W3208911061 hasRelatedWork W2488765804 @default.
• W3208911061 hasRelatedWork W2566834682 @default.
• W3208911061 hasRelatedWork W2659551104 @default.
• W3208911061 hasRelatedWork W2783639445 @default.
• W3208911061 hasRelatedWork W2915260374 @default.
• W3208911061 hasRelatedWork W2981481304 @default.
• W3208911061 hasRelatedWork W3089931053 @default.
• W3208911061 hasRelatedWork W3183817008 @default.
• W3208911061 hasRelatedWork W189205490 @default.
• W3208911061 isParatext "false" @default.
• W3208911061 isRetracted "false" @default.
• W3208911061 magId "3208911061" @default.
• W3208911061 workType "dissertation" @default.