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-# Use-case requirements+===== Use-case requirements =====
  
 This chapter defines the minimum requirements for the use cases that will be developed, validated, and comparatively assessed within the SafeAV framework (AV shuttle, F1TENTH, mobile robot, UAV). The goal is to align learning outcomes with technical, safety, and regulatory constraints, and to ensure smooth integration with the selected toolchain (e.g., Autoware/ROS2, simulators, and V&V tools). Requirements cover system boundaries and assumptions, environment and scenario descriptions, data flows, performance and safety targets, acceptance criteria, and end-to-end traceability to course outcomes and WP-level KPIs. We explicitly emphasize compliance with relevant standards and regulations (e.g., UNECE; EASA, where applicable), educational reusability (SITL/HITL), and reproducibility: each use case must ship with standardized scenarios, test scripts, and evaluation report templates. Use cases are selected to cover a wide range of AV, both in the ground and air domains.  This chapter defines the minimum requirements for the use cases that will be developed, validated, and comparatively assessed within the SafeAV framework (AV shuttle, F1TENTH, mobile robot, UAV). The goal is to align learning outcomes with technical, safety, and regulatory constraints, and to ensure smooth integration with the selected toolchain (e.g., Autoware/ROS2, simulators, and V&V tools). Requirements cover system boundaries and assumptions, environment and scenario descriptions, data flows, performance and safety targets, acceptance criteria, and end-to-end traceability to course outcomes and WP-level KPIs. We explicitly emphasize compliance with relevant standards and regulations (e.g., UNECE; EASA, where applicable), educational reusability (SITL/HITL), and reproducibility: each use case must ship with standardized scenarios, test scripts, and evaluation report templates. Use cases are selected to cover a wide range of AV, both in the ground and air domains. 
  
-## Use Case #1 AV Shuttle+===== Use Case #1 AV Shuttle =====
  
 The **TalTech iseAuto AV shuttle** is Estonia’s first self-driving vehicle developed as an academic–industry collaboration led by Tallinn University of Technology. TalTech iseAuto operates as a fully electric vehicle with a top speed of approximately 25 km/h and a capacity of up to eight passengers. It can run for around eight hours on a single charge, making it well-suited for short urban routes and campus loops. The shuttle is equipped with a comprehensive perception system that includes three LiDAR sensors and five cameras, providing 360-degree environmental awareness. Navigation is based on pre-mapped routes, while a remote control room enables teleoperation and system monitoring when necessary. Within TalTech, iseAuto serves as a research and educational platform that bridges theoretical learning and real-world experimentation in autonomous driving. The shuttle integrates with the Autoware open-source software stack for perception, planning, and control, and it supports a digital twin simulation environment that allows testing of algorithms in virtual conditions before deploying them on the physical vehicle. This approach has made iseAuto an essential testbed for validating autonomous vehicle safety, sensor fusion, and human–machine interaction. The **TalTech iseAuto AV shuttle** is Estonia’s first self-driving vehicle developed as an academic–industry collaboration led by Tallinn University of Technology. TalTech iseAuto operates as a fully electric vehicle with a top speed of approximately 25 km/h and a capacity of up to eight passengers. It can run for around eight hours on a single charge, making it well-suited for short urban routes and campus loops. The shuttle is equipped with a comprehensive perception system that includes three LiDAR sensors and five cameras, providing 360-degree environmental awareness. Navigation is based on pre-mapped routes, while a remote control room enables teleoperation and system monitoring when necessary. Within TalTech, iseAuto serves as a research and educational platform that bridges theoretical learning and real-world experimentation in autonomous driving. The shuttle integrates with the Autoware open-source software stack for perception, planning, and control, and it supports a digital twin simulation environment that allows testing of algorithms in virtual conditions before deploying them on the physical vehicle. This approach has made iseAuto an essential testbed for validating autonomous vehicle safety, sensor fusion, and human–machine interaction.
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 The AV Shuttle use case requires a flexible and scalable V&V setup that supports both low- and high-fidelity simulations, OpenSCENARIO-based scenario testing, and full integration with Autoware.Universe stack. The environment must enable automated safety and performance evaluation, containerized deployment, and open-source accessibility suitable for higher education. It emphasizes iterative, hands-on experimentation and long-term sustainability without reliance on commercial tools The AV Shuttle use case requires a flexible and scalable V&V setup that supports both low- and high-fidelity simulations, OpenSCENARIO-based scenario testing, and full integration with Autoware.Universe stack. The environment must enable automated safety and performance evaluation, containerized deployment, and open-source accessibility suitable for higher education. It emphasizes iterative, hands-on experimentation and long-term sustainability without reliance on commercial tools
  
-## Use Case #2 F1TENTH+===== Use Case #2 F1TENTH ===== 
  
 The F1TENTH platform is an open-source, small-scale autonomous racing car designed for research and education in autonomous systems. Built on a 1/10-scale RC chassis, it integrates sensors such as LiDAR, camera, and IMU, all running on a ROS2-based control stack. Its modular architecture allows experiments in perception, planning, and control, while remaining low-cost and portable for classroom and laboratory use. The platform is supported by an active international community, offering simulation environments, datasets, and open course materials that make it ideal for hands-on learning and benchmarking in robotics and self-driving research. In academia, it serves as a standardized benchmark for teaching autonomous driving algorithms, allowing students to bridge theory and practice through competitions, lab assignments, and project-based learning. Universities use F1TENTH to demonstrate safety validation, sensor fusion, and real-time decision-making concepts within a manageable and reproducible framework, making it an ideal entry point for higher education in robotics and autonomous vehicle research. The F1TENTH platform is an open-source, small-scale autonomous racing car designed for research and education in autonomous systems. Built on a 1/10-scale RC chassis, it integrates sensors such as LiDAR, camera, and IMU, all running on a ROS2-based control stack. Its modular architecture allows experiments in perception, planning, and control, while remaining low-cost and portable for classroom and laboratory use. The platform is supported by an active international community, offering simulation environments, datasets, and open course materials that make it ideal for hands-on learning and benchmarking in robotics and self-driving research. In academia, it serves as a standardized benchmark for teaching autonomous driving algorithms, allowing students to bridge theory and practice through competitions, lab assignments, and project-based learning. Universities use F1TENTH to demonstrate safety validation, sensor fusion, and real-time decision-making concepts within a manageable and reproducible framework, making it an ideal entry point for higher education in robotics and autonomous vehicle research.
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 The F1TENTH use case focuses on providing an open, reproducible, and educational platform for validating perception, planning, and control algorithms in small-scale autonomous vehicles. Its simulation and interface requirements emphasize lightweight, ROS2-compatible environments that run efficiently on standard hardware, enabling hands-on learning, iterative testing, and scalable experimentation in autonomous systems education. The F1TENTH use case focuses on providing an open, reproducible, and educational platform for validating perception, planning, and control algorithms in small-scale autonomous vehicles. Its simulation and interface requirements emphasize lightweight, ROS2-compatible environments that run efficiently on standard hardware, enabling hands-on learning, iterative testing, and scalable experimentation in autonomous systems education.
  
-## Use Case #3 Mobile Robot+===== Use Case #3 Mobile Robot ===== 
  
 The Mobile Robot (RTU) use case focuses on cooperative indoor logistics systems designed to demonstrate autonomous navigation, coordination, and task management in controlled environments. The setup consists of two mobile robot platforms, a central server for planning and task distribution, and MQTT-based communication for asynchronous message exchange. Each robot operates under a ROS2-based control architecture integrating LiDAR, camera, and deep learning–based segmentation for enhanced mapping and path planning. Within the academic context, this use case provides a practical environment for teaching multi-robot coordination, communication reliability, and safety validation, bridging theoretical concepts in robotics and AI with real-world industrial applications. The Mobile Robot (RTU) use case focuses on cooperative indoor logistics systems designed to demonstrate autonomous navigation, coordination, and task management in controlled environments. The setup consists of two mobile robot platforms, a central server for planning and task distribution, and MQTT-based communication for asynchronous message exchange. Each robot operates under a ROS2-based control architecture integrating LiDAR, camera, and deep learning–based segmentation for enhanced mapping and path planning. Within the academic context, this use case provides a practical environment for teaching multi-robot coordination, communication reliability, and safety validation, bridging theoretical concepts in robotics and AI with real-world industrial applications.
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 The defined V&V requirements establish a comprehensive validation chain that connects design, simulation, and real-world testing. They emphasize fault tolerance, runtime monitoring, and reproducibility using open-source ROS2 and MQTT-based architectures. This ensures that students can study and experiment with advanced verification techniques while developing safe and resilient autonomous robotic systems. The defined V&V requirements establish a comprehensive validation chain that connects design, simulation, and real-world testing. They emphasize fault tolerance, runtime monitoring, and reproducibility using open-source ROS2 and MQTT-based architectures. This ensures that students can study and experiment with advanced verification techniques while developing safe and resilient autonomous robotic systems.
  
-## Use Case #4 Drone+===== Use Case #4 Drone ===== 
  
 The Drone (SUT, PRO) use case focuses on unmanned aerial vehicle (UAV) systems that bridge aviation safety principles with autonomous mobility education. Developed through Prodron’s extensive experience in UAV training and system design, this use case explores real-world challenges such as emergency response, navigation under uncertainty, sensor fusion, and communication reliability. The drones operate using open-source frameworks like ArduPilot and QGroundControl, supporting both software-in-the-loop (SIL) and hardware-in-the-loop (HIL) validation. In the academic context, this use case provides a versatile platform for teaching autonomous flight control, safety verification, and resilience testing, allowing students to apply V&V methodologies from aerial robotics to broader autonomous vehicle domains. The Drone (SUT, PRO) use case focuses on unmanned aerial vehicle (UAV) systems that bridge aviation safety principles with autonomous mobility education. Developed through Prodron’s extensive experience in UAV training and system design, this use case explores real-world challenges such as emergency response, navigation under uncertainty, sensor fusion, and communication reliability. The drones operate using open-source frameworks like ArduPilot and QGroundControl, supporting both software-in-the-loop (SIL) and hardware-in-the-loop (HIL) validation. In the academic context, this use case provides a versatile platform for teaching autonomous flight control, safety verification, and resilience testing, allowing students to apply V&V methodologies from aerial robotics to broader autonomous vehicle domains.
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 The UAV V&V requirements ensure comprehensive testing of autonomous flight systems under realistic and variable conditions, supporting both educational and research-oriented objectives. By integrating open-source simulation, communication reliability testing, and hardware-in-the-loop validation, they provide a robust foundation for safety assurance and hands-on learning. The SafeAV study recommends a dual-tier approach for UAV simulation and validation—combining commercial off-the-shelf (COTS) systems for rapid onboarding with open-source ecosystems for advanced, research-driven experimentation. This structure enables both immediate applicability in training contexts and long-term scalability for integration into academic courses, laboratories, and testbeds. The UAV V&V requirements ensure comprehensive testing of autonomous flight systems under realistic and variable conditions, supporting both educational and research-oriented objectives. By integrating open-source simulation, communication reliability testing, and hardware-in-the-loop validation, they provide a robust foundation for safety assurance and hands-on learning. The SafeAV study recommends a dual-tier approach for UAV simulation and validation—combining commercial off-the-shelf (COTS) systems for rapid onboarding with open-source ecosystems for advanced, research-driven experimentation. This structure enables both immediate applicability in training contexts and long-term scalability for integration into academic courses, laboratories, and testbeds.
  
-Conclusions and Decisions+===== Conclusions and Decisions ===== 
 In conclusion, the consortium has evaluated the available verification and validation frameworks based on current research, technical feasibility, and educational applicability. The resulting decisions reflect a balanced consideration of open-source maturity, interoperability, and relevance to the specific SafeAV use cases. The selected frameworks demonstrate strong community support, active ongoing development, and proven suitability for academic integration. Their open-source nature ensures transparency, adaptability, and long-term sustainability, while their functionality aligns closely with the technical and pedagogical goals defined for each use case. In conclusion, the consortium has evaluated the available verification and validation frameworks based on current research, technical feasibility, and educational applicability. The resulting decisions reflect a balanced consideration of open-source maturity, interoperability, and relevance to the specific SafeAV use cases. The selected frameworks demonstrate strong community support, active ongoing development, and proven suitability for academic integration. Their open-source nature ensures transparency, adaptability, and long-term sustainability, while their functionality aligns closely with the technical and pedagogical goals defined for each use case.
 Use case No Use case No
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 CARLA (Unreal Engine based simulator) CARLA (Unreal Engine based simulator)
  
-# Key Findings and Recommendations+===== Key Findings and Recommendations ===== 
  
 ROS-based frameworks thus form a critical part of the **SafeAV educational toolchain,** ensuring scalability from lightweight student projects to advanced V&V experiments in research and industrial contexts. ROS-based frameworks thus form a critical part of the **SafeAV educational toolchain,** ensuring scalability from lightweight student projects to advanced V&V experiments in research and industrial contexts.
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 4. Partner use cases ensure coverage of ground, aerial, and hybrid autonomous systems for educational demonstration. 4. Partner use cases ensure coverage of ground, aerial, and hybrid autonomous systems for educational demonstration.
  
-## Next Steps → T4.2 Adaptation+===== Next Steps → T4.2 Adaptation ===== 
  
 - Containerize selected frameworks for student deployment. - Containerize selected frameworks for student deployment.
en/safeav/handson.1761810284.txt.gz · Last modified: 2025/10/30 07:44 by raivo.sell
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