Objectives: The study aims to improve road safety by developing a lane detection model that achieves high accuracy even in adverse conditions like poor lighting, occlusions, and inclement weather. In high-traffic countries like India, where poor lane discipline contributes to road accidents, this system can significantly enhance driver assistance. Methods: This study compares three approaches—Canny Edge Detection, Hough Transform, and a CNN-based model. The CNN was trained on a dataset of over 1,000 annotated images featuring diverse scenarios, including curves, shadows, and varied lighting conditions. Evaluation metrics include precision, recall, F1-score, and computational latency. Findings: The CNN-based model achieved an accuracy of 96%, and F1-score of 0.92, outperforming traditional methods, which showed an average accuracy of 70% and F1-score of 68%. It maintained robustness under low-light conditions (accuracy: 91%) and adverse weather (F1-score: 89%). Real-time frame processing improved to 25 FPS, marking a 30% enhancement over baseline. Novelty: By leveraging CNNs for feature extraction and training on a diverse dataset, this study demonstrates a significant improvement over traditional methods. The proposed approach bridges the gap between accuracy and real-time application, offering a viable solution for lane detection in challenging environments. Keywords: Accidents, Computer Vision, ML, CNN, Lane-Detection

In recent years, there is a growing interest towards Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) that have the highest possibility of lowering pollutants. The propulsion batteries of EVs are charged from the grid using an onboard charger. The chargers are a critical component of any electric vehicle, which requires high-efficiency charger with a high-power factor. This project proposes a battery charger consisting of a resonance circuit and step-up AC-DC converter that performs bridgeless single-power conversion. The input diode rectifier with conduction losses are reduced by using a bridgeless arrangement. By offering Zero Current Switching, the resonance circuit decreases the losses that occur across the diodes. Furthermore, inoculation of current due to the resonance circuit allows the transformer to excite the core in both directions, providing for high power capabilities. The efficiency of the charger and the quality of the power along with its harmonic are used to assess its performance. Furthermore, EV has inbuilt charger and so it must be tiny, light, and quick charging. In MATLAB/SIMULINK, the suggested converter is constructed and simulated. The suggested charger would use a method obtained through feedback linearization to adjust the correction in any power variations in a one-step power conversion.