Intersection navigation in Duckietown using 3D image features is an approach intented to improve autonomous intersection navigation, enhancing decision-making and path planning in complex Duckietown environments, i.e., made of several road loops and road intersections. 

The traditional approach to intersection navigation in Duckietown is naive: (a) stop at the red line before the intersection, (b) read Apriltag-equipped traffic signs (providing information on the shape and coordination mechanism at intersections); (c) decide which direction to take; (d) coordinate with other vehicles at the intersection to avoid collisions; (e) navigate through the intersection. This last step is performed in an open-loop fashion, leveraging the known appearance specifications of intersections in Duckietown. 

By incorporating 3D image features in the perception pipeline, extrapolated from the Duckietown road lines, Duckiebots can achieve a representation of their pose while crossing the intersection, closing, therefore, the loop and improving navigation accuracy, in addition to facilitating the development of new strategies for intersection navigation, such as real-time path optimization. 

Combining 3D image features with methods, such as Bird’s Eye View (BEV) transformations allows for comprehensive representations of the intersection. The integration of these techniques improves the accuracy of stop line detection and obstacle avoidance contributes to advancing autonomous navigation algorithms and supports real-world deployment scenarios.

Image sensors are ubiquitous for their well-known sensory traits (e.g., distance measurement, robustness, accessibility, variety of form factors, etc.). Achieving autonomy with monocular vision, i.e., using only one image sensor, is desirable, and much work has gone into approaches to achieve this task. Duckietown’s first Duckiebot, the DB17, was designed with only a camera as sensor suite to highlight the importance of this challenge!  

But images, due to the integrative nature of image sensors and the physics of the image generation process, are subject to motion blur, occlusions, and sensitivity to environmental lighting conditions, which challenge the effectiveness of “traditional” computer vision algorithms to extract information. 

In this work, the author uses “LEDNet” to mitigate some of the known limitations of image sensors for use in autonomy. LEDNet’s encoder-decoder architecture with high resolution enables lane-following and obstacle detection. The model processes images at high frame rates, allowing recognition of turns, bends, and obstacles, which are useful for timely decision-making. The resolution improves the ability to differentiate road markings from obstacles, and classification accuracy.

LEDNet’s obstacle-avoidance algorithm can classify and detect obstacles even at higher speeds. Unlike Vision Transformers (wiki) (ViT) models, LEDNet avoids missing parts of obstacles, preventing robot collisions.

The model handles small obstacles by identifying them earlier and navigating around them. In the simulated Duckietown environment, LEDNet outperforms other models in lane-following and obstacle-detection tasks.

LEDNet uses “real-time” image segmentation to provide the Duckiebot with information for steering decisions. While the study was conducted in a simulation, the model’s performance indicates it would work in real-world scenarios with consistent lighting and predictable obstacles.

The next is to try it out! 

This thesis explores the use of reinforcement learning (RL) techniques to enable autonomous navigation in the Duckietown. Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback through rewards or penalties. The goal is to maximize long-term rewards.

This work focuses on implementing and comparing various RL algorithms—specifically Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) – to analyze performance in autonomous navigation. RL enables agents to learn behaviors by interacting with their environment and adapting to dynamic conditions. The PPO model was found demonstrating smooth driving using grayscale images for enhanced computational efficiency.

Another feature of this project is the integration of YOLO v5, an object detection model, which allowed the Duckiebot to recognize and stop for obstacles, improving its safety capabilities. This integration of perception and RL enabled the Duckiebot not only to follow lanes but also to navigate autonomously, making ‘real-time’ adjustments based on its surroundings.

By transferring trained models from simulation to physical Duckiebots (Sim2Real), the thesis evaluates the feasibility of applying these models to real-world autonomous driving scenarios. This work showcases how reinforcement learning and object detection can be combined to advance the development of safe, autonomous navigation systems, providing insights that could eventually be adapted for full-scale vehicles.

Autonomous driving is already inherently hard. Driving at night makes it even more challenging! This is why smart lighting is an interesting application that intersects with autonomous driving: having city infrastructure, such as traffic lights and watchtowers, generate dynamically varying light – only where and when they’re needed – to make driving at night not only possible but safe. Here are some reasons for which this project is interesting:

Realistic driving scenarios: autonomous driving systems must handle varying lighting conditions. Day and night cycles are just the beginning: transitions like sunrise or sunset make the spectrum of experimental corner cases more complex, hence Duckietown a valuable testbed.

Robust lane-following capabilities: developing an adaptive lighting system in which the city infrastructure “collaborates” with Duckiebot to provide optimal driving scenarios reinforces driving performances and general robustness for lane following.  

Decentralized control for scalability: a decentralized approach to managing lighting implies that the system can be scalable across Duckietowns of arbitrary dimensions, making it more adaptable and resilient.

Autonomous lighting management: a responsive street lighting system, working in tandem with the Duckiebot’s onboard sensors, improves energy efficiency and ensures safety by adjusting to local lighting needs automatically.