Advanced 3D perception based on 2D projection and sensor fusion for autonomous vehicles
Deep learning (DL) has experienced rapid growth in recent years due to its universal learning approach, robustness, generalization, and scalability. Furthermore, the field of autonomous driving cars (ADC) is continuously advancing, with the prospect of fully automated public vehicles becoming a near...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/404036 |
| Acceso en línea: | https://hdl.handle.net/2117/404036 |
| Access Level: | acceso abierto |
| Palabra clave: | Automated vehicles Artificial intelligence LiDAR Artificial Intelligence object detection perception autonomous vehicles Light Detection And Ranging Vehicles autònoms Intel·ligència artificial Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica::Fotònica |
| Sumario: | Deep learning (DL) has experienced rapid growth in recent years due to its universal learning approach, robustness, generalization, and scalability. Furthermore, the field of autonomous driving cars (ADC) is continuously advancing, with the prospect of fully automated public vehicles becoming a near-future reality. In this context, a critical safety aspect for ADCs revolves around the precise recognition of pedestrians and obstacles. This thesis aims to tackle this challenge by using 2D images obtained from the projections of the 3D LiDAR data. With those 2D images, we will be able to use fully developed 2D neural networks instead of 3D neural networks that are less mature. We compared our method and results with the results obtained using only RGB raw images to check its effectiveness, but using the RGB images led to better outcomes. |
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