Reproducible Sim-to-Real Traffic Signal Control Environment

Posted on June 12, 2025 | by Duckietown Admin

General Information

Title: Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control
Authors: Yiran Zhang, Khoa Vo, Longchao Da, Tiejin Chen, Xiaoou Liu, Hua Wei
Institution: Arizona State University, USA
Citation: Zhang, Y., Vo, K., Da, L., Chen, T., Liu, X. and Wei, H., 2025, May. Reproducible and Low-cost Sim-to-Real Environment for Traffic Signal Control. In Proceedings of the ACM/IEEE 16th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2025) (pp. 1-2).

Reproducible Sim-to-Real Traffic Signal Control Environment

As urban environments become increasingly populated and automobile traffic soars, with US citizens spending on average 54 hours a year stuck on the roads, active traffic control management promises to mitigate traffic jams while maintaining (or improving) safety.

LibSignal++ is a Duckietown-based testbed for reproducible and low-cost sim-to-real evaluation of traffic signal control (TSC) algorithms. Using Duckietown enables consistent, small-scale deployment of both rule-based and learning-based TSC models.

LibSignal++ integrates visual control through camera-based sensing and object detection via the YOLO-v5 model. It features modular components, including Duckiebots, signal controllers, and an indoor positioning system for accurate vehicle trajectory tracking. The testbed supports dynamic scenario replication by enabling both manual and automated manipulation of sensor inputs and road layouts.

Key aspects of the research include:

Sim-to-real pipeline for Reinforcement Learning (RL)-based traffic signal control training and deployment
Multi-simulator training support with SUMO, CityFlow, and CARLA
Reproducibility through standardized and controllable physical components
Integration of real-world sensors and visual control systems
Comparative evaluation using rule-based policies on 3-way and 4-way intersections

The work concludes with plans to extend to Machine Learning (ML)-based TSC models and further sim-to-real adaptation.

Highlights - Reproducible Sim-to-Real Traffic Signal Control Environment

Here is a visual tour of the sim-to-real work of the authors. For all the details, check out the full paper.

Abstract

Here is the abstract of the work, directly in the words of the authors:

This paper presents a unique sim-to-real assessment environment for traffic signal control (TSC), LibSignal++, featuring a 14-ft by 14-ft scaled-down physical replica of a real-world urban roadway equipped with realistic traffic sensors such as cameras, and actual traffic signal controllers. Besides, it is supported by a precise indoor positioning system to track the actual trajectories of vehicles. To generate various plausible physical conditions that are difficult to replicate with computer simulations, this system supports automatic sensor manipulation to mimic observation changes and also supports manual adjustment of physical traffic network settings to reflect the influence of dynamic changes on vehicle behaviors. This system will enable the assessment of traffic policies that are otherwise extremely difficult to simulate or infeasible for full-scale physical tests, providing a reproducible and low-cost environment for sim-to-real transfer research on traffic signal control problems.

Results

Three traffic control policies were tested over a number of experiment repetitions, evaluating each time traffic throughput, average vehicle waiting times, and vehicle battery consumption. Standard deviations for all policies were found to be within acceptable ranges, leading the authors to confirm the ability of the testbed to deliver reproducible results within controlled environments.

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Project Authors

Yiran Zhang is associated with the Arizona State University, USA.

Khoa Vo is associated with the Arizona State University, USA.

Longchao Da is pursuing his Ph.D. at the Arizona State University, USA.

Tiejin Chen is pursuing his Ph.D. at the Arizona State University, USA.

Xiaoou Liu is pursuing her Ph.D. at the Arizona State University, USA.

Hua Wei is an Assistant Professor at the School of Computing and Augmented Intelligence, Arizona State University, USA.

Learn more

Duckietown is a platform for creating and disseminating robotics and AI learning experiences.

It is modular, customizable and state-of-the-art, and designed to teach, learn, and do research. From exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge, Duckietown evolves with the skills of the user.

Autonomous Navigation System Development in Duckietown

Posted on May 31, 2025 | by Duckietown Admin

Autonomous Navigation System Development in Duckietown

Project Resources

Objective: Enable safe and reliable autonomous navigation for Duckiebots in Duckietowns.
Approach: Use onboard sensors, computer vision, and Dijkstra algorithm to navigate lanes, intersections, and avoid obstacles.
Authors: Julien-Alexandre Bertin Klein, Andrea Pellegrin, Fathia Ismail from the Technical University of Munich, Germany.

Project highlights

Camera and line detector for autonomous navigation — Detecting and stopping at the stop line

AprilTag and line detector views for autonomous navigation — Detecting intersections and executing turns

Dijkstra algorithm map for autonomous navigation — Transforming Roads into a Navigable Network

Autonomous Navigation System Development in Duckietown - the objectives

The primary objective of this project is to develop and refine an Autonomous Navigation System within the Duckietown environment, leveraging ROS-based control and computer vision to enable reliable lane following and safe intersection navigation. This includes calibrating sensor inputs, particularly from the camera, IMU, and encoders, and integrating advanced algorithms such as Dijkstra algorithm for optimal path planning. The project aims to ensure that the Duckiebot can autonomously detect lanes, stop lines, and obstacles while dynamically computing the shortest path to any designated point within the mapped environment. Additionally, the system is designed to transition smoothly between operational states (lane following, intersection handling, and recovery) using a refined Finite State Machine approach, all while maintaining robust communication within the ROS ecosystem.

Project Report

The challenges and approach

The project faced several challenges, beginning with hardware constraints, such as the physical limitations of wheel traction and battery lifespan, which affected motion stability and operational time. The integration of various ROS packages, some with incomplete documentation and inconsistent coding practices, complicated the development of a reliable and maintainable codebase. The method adopted involved precise sensor calibration to ensure accurate perception and control, incorporating camera intrinsic and extrinsic calibration for improved visual data interpretation, and adjusting wheel parameters to maintain balanced motion. The lane following module required parameter tuning for gain, trim, and heading correction to adapt to Duckietown’s environment. The original FSM-based intersection navigation system was re-engineered due to unreliability in node transitions, replaced with a distance-based approach for intersection stops and turns, ensuring deterministic and reliable behavior. Dijkstra’s algorithm was implemented to create a structured graph representation of the city map, enabling dynamic path planning that adapts to real-time inputs from the perception system. Custom web dashboards built with React.js and roslibjs facilitated monitoring and debugging by providing live data feedback and control interfaces. Through this rigorous and iterative process, the project achieved a robust autonomous navigation system capable of precise path planning and safe maneuvering within Duckietown.

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Autonomous Navigation System Development in Duckietown: Authors

Julien-Alexandre Bertin Klein is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Andrea Pellegrin is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Fathia Ismail is currently a Bachelor of Science (BSc.), Information Engineering at the Technical University of Munich, Germany.

Learn more

Duckietown is a modular, customizable, and state-of-the-art platform for creating and disseminating robotics and AI learning experiences.

Duckietown is designed to teach, learn, and do research: from exploring the fundamentals of computer science and automation to pushing the boundaries of knowledge.

These spotlight projects are shared to exemplify Duckietown’s value for hands-on learning in robotics and AI, enabling students to apply theoretical concepts to practical challenges in autonomous robotics, boosting competence and job prospects.

Autonomous Navigation and Parking in Duckietown

Posted on May 18, 2025 | by Duckietown Admin

Autonomous Navigation and Parking in Duckietown

Project Resources

Objective: Achieve autonomous navigation and parking in an intersection-equipped Duckietown city.
Approach: Utilizing an FSM to transition between PID control-based lane following, intersection navigation, and parking using visual fiducial markers (AprilTags), color masking, and dead reckoning.
Authors: Eric Khumbata, Cameron Hildebrandt, Jasper Eng from the University of Alberta.
Course: This project is part of the CMPUT 412 Experimental Mobile Robotics course at the University of Alberta under Prof. Matthew E. Taylor.

Project highlights

Static parameters in a dynamic environment are pre-programmed failure points.

Autonomous Navigation and Parking in Duckietown using pid control — Figure 1. Lane Following with AprilTag Decision Logic

Autonomous Navigation and Parking in Duckietown: the objectives

This includes the development of a closed-loop PID control mechanism for continuous lane following, the use of AprilTag detection for intersection decision-making, and a state-driven behavior architecture to transition between tasks such as stopping, turning, and parking.

The system uses wheel encoder data for dead-reckoning-based motion execution in the absence of visual cues, and applies HSV-based color segmentation to detect and respond to static and dynamic obstacles. Visual servoing is used for parking alignment based on AprilTag localization. The control logic is modular and supports parameter tuning for hardware variability, with temporal filtering to suppress redundant detections and ensure stability.

General Information

Title: An Adaptive Obstacle Avoidance Model for Autonomous Robots Based on Dual-Coupling Grouped Aggregation and Transformer Optimization
Authors: Yuhu Tang, Ying Bai, and Qiang Chen
Institution: Hefei University, China
Citation: Tang, Y., Bai, Y., & Chen, Q. (2025). An Adaptive Obstacle Avoidance Model for Autonomous Robots Based on Dual-Coupling Grouped Aggregation and Transformer Optimization. Sensors, 25(6), 1839. https://doi.org/10.3390/s25061839

Transformer Visual Control for Dynamic Obstacle Avoidance

This work details a transformer visual control approach for autonomous robotic obstacle avoidance in dynamic environments. It introduces the GAS-H-Trans model, which integrates a dual-coupling grouped aggregation strategy with transformer-based attention mechanisms.

Key components of the approach include grouped spatial feature aggregation, Harris hawk optimization (HHO) for parameter tuning, and semantic segmentation for real-time visual perception. The output of the segmentation is used to compute potential fields for navigation. An artificial potential field (APF) method, further optimized using particle swarm optimization (PSO), enhances obstacle avoidance. The system was evaluated in Unity3D virtual environments and on datasets including KITTI, and ImageNet.

The model architecture improves local and global feature extraction, enabling adaptive navigation. Simulation results demonstrate that GAS-H-Trans outperforms baseline models in segmentation accuracy and avoidance reliability. The implementation uses Transformer structures, self-attention, and heuristic optimization for enhanced environmental understanding.

Experiments using Duckietown-based simulations confirm that the proposed Transformer Visual Control strategy with GAS-H-Trans significantly improves obstacle avoidance reliability with respect to typical approaches.

Highlights - Transformer Visual Control for Dynamic Obstacle Avoidance

Here is a visual tour of this work. For all the details, check out the full paper.

GAS-H-Trans Transformer Visual Control system architecture for Duckietown robotic navigation — Figure 1. GAS-H-Trans Framework Architecture

Transformer Visual Control model architecture using GAS-H-Trans and grouped aggregation in Duckietown simulation — Figure 2. Detailed Architecture of GAS-H-Trans Model

PSO-optimized artificial potential field for Transformer Visual Control in Duckietown simulation — Figure 3. Artificial Potential Field Optimization Flow

Transformer Visual Control model accuracy chart in Duckietown obstacle avoidance task — Figure 4. Model Accuracy Comparison Across Epochs

Swin Transformer comparison with GAS-H-Trans in Transformer Visual Control on Duckietown data — Figure 6. Swin Transformer vs GAS-H-Trans Performance

Duckietown virtual environment dataset creation for Transformer Visual Control experiments — Figure 7. Dataset Creation Process

GAS-H-Trans Transformer Visual Control segmentation accuracy in Duckietown virtual dataset — Figure 8. Image Segmentation Accuracy in Duckietown Scene

Segmentation output of Transformer Visual Control with JSON mapping in Duckietown task — Figure 9. Segmentation Mask and Data Structure

Transformer Visual Control path planning with GAS-H-Trans and PSO in Duckietown — Figure 10. Obstacle Avoidance in Duckietown Environment

Comparison of traditional and PSO-optimized APF for Transformer Visual Control in Duckietown — Figure 11. Effect of PSO Optimization on APF

Enhanced Transformer Visual Control robot navigation path in Duckietown simulation — Figure 12. Optimized Obstacle Avoidance Path

Transformer Visual Control success rate with traditional vs PSO-optimized APF in Duckietown — Figure 13. Obstacle Avoidance Success Rate

: Figure 1. GAS-H-Trans Framework Architecture

: Figure 2. Detailed Architecture of GAS-H-Trans Model

: Figure 3. Artificial Potential Field Optimization Flow

: Figure 4. Model Accuracy Comparison Across Epochs

: Figure 5. Segmentation Output Comparison

: Figure 6. Swin Transformer vs GAS-H-Trans Performance

: Figure 7. Dataset Creation Process

: Figure 8. Image Segmentation Accuracy in Duckietown Scene

: Figure 9. Segmentation Mask and Data Structure

: Figure 10. Obstacle Avoidance in Duckietown Environment

: Figure 11. Effect of PSO Optimization on APF

: Figure 12. Optimized Obstacle Avoidance Path

: Figure 13. Obstacle Avoidance Success Rate

Abstract

In the author’s words:

Accurate obstacle recognition and avoidance are critical for ensuring the safety and operational efficiency of autonomous robots in dynamic and complex environments. Despite significant advances in deep-learning techniques in these areas, their adaptability in dynamic and complex environments remains a challenge. To address these challenges, we propose an improved Transformer-based architecture, GAS-H-Trans.

This approach uses a grouped aggregation strategy to improve the robot’s semantic understanding of the environment and enhance the accuracy of its obstacle avoidance strategy. This method employs a Transformer-based dual-coupling grouped aggregation strategy to optimize feature extraction and improve global feature representation, allowing the model to capture both local and long-range dependencies.

The Harris hawk optimization (HHO) algorithm is used for hyperparameter tuning, further improving model performance. A key innovation of applying the GAS-H-Trans model to obstacle avoidance tasks is the implementation of a secondary precise image segmentation strategy. By placing observation points near critical obstacles, this strategy refines obstacle recognition, thus improving segmentation accuracy and flexibility in dynamic motion planning. The particle swarm optimization (PSO) algorithm is incorporated to optimize the attractive and repulsive gain coefficients of the artificial potential field (APF) methods.

This approach mitigates local minima issues and enhances the global stability of obstacle avoidance. Comprehensive experiments are conducted using multiple publicly available datasets and the Unity3D virtual robot environment. The results show that GAS-H-Trans significantly outperforms existing baseline models in image segmentation tasks, achieving the highest mIoU (85.2%). In virtual environment obstacle avoidance tasks, the GAS-H-Trans + PSO-optimized APF framework achieves an impressive obstacle avoidance success rate of 93.6%. These results demonstrate that the proposed approach provides superior performance in dynamic motion planning, offering a promising solution for real-world autonomous navigation applications.

Conclusion - Transformer Visual Control for Dynamic Obstacle Avoidance

Here is the author’s summary and overview of lessons learned from this work:

In this study, we proposed the GAS-H-Trans framework for image segmentation and dynamic obstacle avoidance in autonomous robots. The key contributions are summarized as follows. (1) Dual-coupling grouped aggregation strategy: A Transformer-based dualcoupling grouped aggregation method optimizes feature extraction and enhances global feature representation, thereby improving the model’s perception performance in dynamic motion planning. (2) Harris hawk optimization (HHO): The integration of the HHO algorithm into the GAS-Trans framework optimizes the number of Transformer layers and iterations, improving model accuracy and reducing computational costs. (3) PSOoptimized artificial potential field (APF): We integrated the PSO algorithm with APF to optimize the attractive and repulsive gain coefficients, addressing local minima issues and enhancing the global stability of the obstacle avoidance system.

This study also proposes a secondary precise image segmentation strategy. By setting the observation points near critical obstacles for fine-tuned segmentation, the flexibility and accuracy of the segmentation model’s environmental perception are effectively enhanced, thereby improving the robot’s obstacle avoidance capabilities.

Through the integration of PSO-optimized APF with image segmentation, the GAS-HTrans + PSO-optimized APF framework demonstrated significant improvements in obstacle avoidance. In the experimental validation of this study, the obstacles remained static throughout the navigation process. Using this method, the autonomous robot dynamically adjusted its obstacle avoidance trajectory based on segmented environmental features. This integration significantly enhanced environmental perception capabilities and the accuracy of obstacle avoidance decisions, enabling more efficient navigation in static obstacle environments.

Extensive experiments on publicly available datasets (Duckiebot, KITTI, ImageNet) and in the Unity3D virtual robot environment validate the effectiveness of the proposed framework. The GAS-H-Trans framework outperformed traditional models in image segmentation tasks, achieving the highest mIoU of 85.2%. Furthermore, in virtual obstacle avoidance experiments, the GAS-H-Trans + PSO-optimized APF framework achieved an obstacle avoidance success rate of 93.6%.

These results effectively validate the proposed strategy, which combines secondary image segmentation from GAS-H-Trans with the PSO-optimized APF method, significantly improving obstacle avoidance performance in dynamic motion planning. Additionally, the GAS-H-Trans framework has the potential to be extended to fully dynamic environments by incorporating real-time object tracking and adaptive obstacle modeling. However, some limitations exist. The majority of the experiments were conducted in simulated environments, and future research will focus on validating the framework in real-world scenarios and improving real-time performance.

Additionally, the integration of multi-modal sensor data (such as LiDAR and ultrasonic sensors) will be an important direction for future work to further enhance environmental perception and robustness.

In conclusion, the new framework offers an innovative solution for autonomous robot obstacle avoidance in dynamic motion planning. Its powerful environmental perception and obstacle avoidance performance demonstrate significant potential for practical applications. With further optimization and real-world validation, this framework will play a crucial role in the future development of autonomous navigation and robotics technology.

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Project Authors

Yuhu Tang is affiliated with the School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

Ying Bai is affiliated with the School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.

Qiang Chen is affiliated with School of Electrical Engineering and Automation
National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology, Hefei University of Technology, Hefei, China, Hefei University, Hefei 230601, China.