Week 0 - TFM Proposal study
Summary
In my initial meeting with my advisor, we outlined the core objectives of my Final Year Project (TFM).
TFM proposal study
Development of a robot’s perception in unstructured environments.
Work planning:
- Contact to Goose dataset developers to find out data availability. Study dataset structure.
- Obtain RELLIS-3D dataset and know its structure.
- Develop semantic segmentation algorithm for testing.
- Study other data sets (RUGD, FIRE, CITYSCAPES).
- Prepare the work environment in Github.
Key Topics in Autonomous Driving
Computer Vision
Computer Vision is a branch of artificial intelligence focused on the processing of visual information. In the context of autonomous driving, it is used to detect objects in the vehicle’s environment and to extract relevant information from images captured by the vehicle’s cameras. Techniques such as image segmentation and feature detection are employed to process visual information.
Neural Networks
Neural Networks are a mathematical model inspired by the biological behavior of neurons and how these are organized in the brain. In the realm of autonomous driving, they are used to make decisions based on processed visual information. Various architectures of neural networks are described, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), along with different databases and simulators used in research.
References
- [Goose Database] https://goose-dataset.de/