Lane Detection for Autonomous Driving with F1Tenth Car

In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to accurately detect its designated driving lane using RGB-D images captured by an onboard camera. The lane detection system must be lightweight to ensure real-time performance, as well as robust against varying lighting conditions and motion blur. The performance of the developed method will be evaluated on hardware in both static and dynamic scenarios.

In this semester thesis, our goal is to enable an F1Tenth car, an autonomous vehicle at 1:10 scale of a Formula 1 car, to accurately detect its designated driving lane using RGB-D images captured by an onboard camera. The lane detection system must be lightweight to ensure real-time performance, as well as robust against varying lighting conditions and motion blur. The performance of the developed method will be evaluated on hardware in both static and dynamic scenarios.

Our objective is to enable the F1Tenth car to autonomously drive on an unknown racing track by perceiving the lane markings through an onboard camera. This thesis focuses primarily on the development of the perception module that can be combined with the existing control stack. We aim to achieve this objective through the following tasks: - Setting up the onboard camera - Developing and tuning the lane extraction method - Camera calibration - Testing the performance of the method on a static car - Investigating motion blur effects when the car is moving Qualifications: - Experience with Python - Computer vision and image processing knowledge - Interest in robotics and hardware applications

Our objective is to enable the F1Tenth car to autonomously drive on an unknown racing track by perceiving the lane markings through an onboard camera. This thesis focuses primarily on the development of the perception module that can be combined with the existing control stack.

We aim to achieve this objective through the following tasks:

- Setting up the onboard camera
- Developing and tuning the lane extraction method
- Camera calibration
- Testing the performance of the method on a static car
- Investigating motion blur effects when the car is moving

Qualifications:

- Experience with Python
- Computer vision and image processing knowledge
- Interest in robotics and hardware applications

To apply for this semester thesis, please send your resume and transcript to Jelena Trisovic (tjelena@ethz.ch).

To apply for this semester thesis, please send your resume and transcript to Jelena Trisovic (tjelena@ethz.ch).