Implementation of a road line detection system using computer vision techniques. The project processes video input to identify and highlight lane lines, essential for autonomous driving applications.

A sophisticated computer vision pipeline for robust lane detection in challenging driving conditions. Features camera calibration, perspective transform, color/gradient thresholding, and polynomial fitting for accurate lane boundary detection.

This ROS2 package simulates real-time GPS and IMU data streams by publishing NavSatFix, Imu, and PoseWithCovarianceStamped messages. Perfect for testing autonomous systems without hardware.

Understanding commonly used hardware for self-driving cars, identifying the main components of the self-driving software stack, programming vehicle modeling and control.

A comprehensive analysis of machine learning models for heart attack prediction using various analytical techniques including EDA, correlation analysis, and predictive modeling.

Implementation of three critical phases of autonomous driving planning: Short-Term, Immediate-Term, and Behavior Planning, focusing on obstacle avoidance and traffic rules.

Application of Convolutional Neural Networks for flower recognition using MobileNetV2 model, utilizing a dataset of 4242 images across five flower types.

Classification of urban noise using various machine learning models including DNN, CNN, LSTM, and Random Forest using the UrbanSound8K dataset.

Implementation of a 2D particle filter to solve the "kidnapped car" problem for vehicle localization. The algorithm predicts positions, matches sensor data to landmarks, updates weights with Gaussian distributions, and resamples particles for accurate vehicle tracking.