Improving the ability of autonomous vehicles to accurately identify and follow lanes in various contexts is crucial. This project aims to provide a novel framework for optimizing a self-driving vehicle that can detect lanes and steer accordingly. A virtual sandbox environment was developed in Unity3D that provides a semi-automated procedural road and driving generation framework for a variety of road scenarios. Four types of segments replicate actual driving situations by directing the car using strategically positioned waypoints. A training dataset thus generated was used to train a behavioral driving model that employs a convolutional neural network to detect the lane and ensure that the car steers autonomously to remain within lane boundaries. The model was evaluated on real-world driving footage from Comma.ai, exhibiting an autonomy of 77% in low challenge road conditions and of 66% on roads with sharper turns.

Hyperbilirubinemia, commonly known as jaundice, is a prevalent condition in newborns, primarily arising from alterations in red blood cell metabolism during the first week of life. While conventional diagnostic methods, such as serum analysis and transcutaneous bilirubinometry, are effective, there remains a critical need for robust, non-invasive, image-based diagnostic tools. In this study, we propose a custom-designed convolutional neural network for classifying jaundice in neonatal images. Image preprocessing and segmentation techniques were systematically evaluated. The optimal workflow, which incorporated contrast enhancement and the extraction of regular skin patches of 144 × 144 pixels from regions of interest segmented using the Segment Anything Model, achieved a testing F1-score of 0.80. Beyond performance, this study addresses numerous shortcomings in the existing literature in this area relating to trust, replicability, and transparency. To this end, we employ fair performance metrics that are more robust to class imbalance, a transparent workflow, share source code, and use Gradient-weighted Class Activation Mapping to visualise and quantify the image regions that influence the classifier’s predictions in pursuit of epistemic justification.