This project uses deep reinforcement learning for autonomous lane following, tackling sim-to-real challenges with domain adaptation and vision-based control.

This project develops a visual obstacle detection system in Duckietown using inverse perspective mapping to improve autonomous navigation accuracy.

Explore how 3D image features enhance Duckiebot intersection navigation in Duckietown, blending cutting-edge BEV tech with hands-on autonomous driving insights.

This project is on monocular navigation in Duckietown using LEDNet, comparing its performance with vision transformer for lane-following and obstacle avoidance.

This thesis applies Reinforcement Learning (RL) for autonomous lane-keeping and YOLO v5 obstacle detection in Duckietown, achieving safe navigation.

What if Duckietowns had smart lighting, so that car and street light fields would combine dynamically for optimal visual perception?

This project uses DBSCAN (Density-Based Algorithm for Discovering Clusters
in Large Spatial Databases with Noise) to improve Duckiebot intersection navigation.

The Obstavoid Algorithm enables obstacle avoidance in Duckietown in realtime, calculating optimal paths using a 3D grid for dynamic, collision-free navigation.

This project by Gianmarco Bernasconi, a former Duckietown student, provided an estimate of the Duckiebot’s pose using a monocular visual odometry approach.

This project enhances Duckiebot planning capabilities for autonomous navigation in Duckietowns using the Dijkstra algorithm.

This project implements robust object detection in Duckietown for Duckiebots under varying lighting conditions and object clutter using a YOLO-based NN.

This student project implements dynamic obstacle avoidance for Duckiebots with the aim of detecting and navigating around static and moving obstacles.