Optimizing Lane Detection and Steering for Self-Driving Cars
Author Information
Author(s): Yordanov Daniel, Chakraborty Ashim, Hasan Md Mahmudul, Cirstea Silvia
Primary Institution: Anglia Ruskin University
Hypothesis
Can a novel framework improve lane detection and steering in autonomous vehicles using deep learning?
Conclusion
The study developed a framework that enables autonomous vehicles to stay within lane boundaries with an autonomy of 77% in low challenge conditions and 66% in more complex scenarios.
Supporting Evidence
- The framework allows for automated data collection, reducing human error.
- The CNN model achieved 77% autonomy in low challenge conditions.
- Data augmentation techniques were employed to enhance the training dataset.
- The model was evaluated using real-world driving footage from Comma.ai.
Takeaway
This study created a computer program that helps self-driving cars learn how to stay in their lanes, even on tricky roads.
Methodology
A virtual environment was created in Unity3D to generate diverse driving scenarios, and a convolutional neural network was trained on the collected data to predict steering angles.
Potential Biases
The model may be biased towards straight driving due to the imbalance in the training dataset.
Limitations
The model struggles with roads that have sharp turns and ambiguous lane markings.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website