Efficient Path Planning and Cruise Control Features In Self-Driving Cars Using ROS2 & Machine Learning

This study proposes an integrated approach that uses machine learning and ROS2 as a combined tool to provide effective path planning and cruise control capabilities in self-driving cars. For object identification and environment perception, the suggested system makes use of Convolutional Neural Networks (CNN) and Haarcascade algorithm. As well as A* and Dijkstra algorithms are applied for finding optimal path and navigation through challenging situations such as T-junctions and cross intersections. Reliable autonomous navigation is demonstrated by the system's efficient integration of motion planning and lane assistance. The model can improve self-driving capabilities, leading to safer and more effective autonomous driving solutions, as demonstrated by experimental findings.