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| Author(s): |
Patrick Yuri Shinzato
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2015-03-26 |
| Examining board members: |
Denis Fernando Wolf;
Arthur de Miranda Neto;
Fernando Santos Osório;
Guilherme Augusto Silva Pereira;
Josue Junior Guimarães Ramos
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| Advisor: | Denis Fernando Wolf |
| Abstract | |
World wide, an estimated 1.2million lives are lostin road crashes each year and Advanced Driver Assistance Systems (ADAS) and Self-driving cars promise to reduce this number. Among the various issues to complete this technology, perception systems are still an unsolved issues. Normally two of them, obstacle detection and road detection, make use of sophisticated algorithms such as supervised machine learning methods which can perform with impressive results if it was trained with good data sets. Since it is a complex and an expensive job to create and maintain data bases of scenarios from the entire world, adaptive and/or self-supervised methods are good candidates for detection systems in the near future. Due that, this thesis present a method to estimate obsta- cles and estimate the road terrain using low cost sensors (stereo camera), avoiding supervised machine learning techniques and the most common assumptions used by works presented in literature. These methods were compared with 3D-LIDAR approaches achieving similar results and thus it can be used as a pre-processing step to improve or allow adaptive methods with machine learning systems. (AU) | |
| FAPESP's process: | 10/01305-1 - A vehicle driving assistance system based on sensor fusion. |
| Grantee: | Patrick Yuri Shinzato |
| Support Opportunities: | Scholarships in Brazil - Doctorate |