This master thesis is a thrilling and fun challenge, where you can compare and evaluate several various lane detection models against each other on a Raspberry Pi! You will get to see how these models behave in real-world driving challenges, testing their characteristics in speed and accuracy. The insights you’ll get from your research and your practical work will be your guidance in the world of small-scale autonomous vehicles and you will get to help tech enthusiasts in the autonomous driving world!
Thesis Type:
Goal and Tasks:
- Define what we’re exploring and limit the scope of the thesis. Then pick two/three (or more) cool lane detection methods to test out. Explore the lane detection techniques and make a little research about what the other researchers have used!
- Gather the datasets (public ones or your own ones) which will serve you as a navigation on this master thesis journey! Make sure that they have everything -> rain, sun, day, night etc.
- Deploy the lane detection models on the Raspberry Pi and get it ready for the real world. Make a big test for the models and record the data, and then measure how well they perform!
- Crunch the numbers and compare the models -> which one is the real gem of them all?
Recommended Prior Knowledge:
- Curiosity, imagination and creativity!
- You need to be comfortable with handling the hardware. The laptop should be yours, and you will be provided with a Raspberry Pi!
- You don’t need to be a coding expert, but a little of coding and data analysis knowledge would be good for this little project. Knowledge in Python would be great!
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