1. Object Detection module for Autonomous Vehicle:
The vehicle uses a camera sensor to detect objects such as pedestrians and stop signs, enabling it to stop accordingly. The ROS interface visualizes the detected objects in real time. Lane following is achieved using an end-to-end CNN-based algorithm.   ↑

2. Autonomous Exploration with LiDAR and Deep Learning:
A mobile robot performs autonomous exploration using a LiDAR-based Potential Field algorithm combined with a deep learning-based object tracker. The system was tested in real-world conditions, with ROS used for execution and visualization.   ↑

3. Semantic Segmentation-based Lane Keeping Assist System:
An advanced lane-keeping system that uses semantic segmentation to identify lane markings and guide the vehicle along its path.   ↑

4. Lane Detection Using Perspective Transformation + LaneNet + Sliding Window:  
A modular approach to lane detection using perspective warping, LaneNet for segmentation, and a sliding window algorithm for tracking lane lines. ↑

5. Semantic Segmentation-based Sidewalk Following Algorithm:

An AI-driven sidewalk navigation algorithm for medium sized vehicle (autonomous delivery vehicle) that uses semantic segmentation to ensure safe and accurate path following in urban environments.  ↑

6. Obstacle Avoidance During Sidewalk Navigation (CAOD Module):

An integrated obstacle avoidance module (CAOD) that operates alongside sidewalk segmentation to enable safe navigation around dynamic and static obstacles.  ↑