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en:safeav:handson:intro [2026/04/29 14:46] – [Use Case #4 Drone] raivo.sellen:safeav:handson:intro [2026/04/29 14:48] (current) – raivo.sell
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-====== Use cases ====== +====== Introduction ======
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-===== Use-case requirements ===== +
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-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.  +
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-===== Conclusions and Decisions ===== +
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-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 +
-Name +
-Description +
-Selected Frameworks and Software Set +
-Responsible partner +
-#1 +
-AV shuttle +
-Simulation-based V&V use case for an autonomous shuttle used in education, prototyping, and pre-deployment testing. Requirements include multi-level simulation, scenario testing with OpenSCENARIO, Autoware.Universe integration, automated safety metrics, containerized deployment, and educational accessibility. Both planning and perception modules are targeted for validation. +
-Autoware.Universe (software under test),  +
-AWSIM (digital twin, sensor simulation, Autoware integration),  +
-CARLA (high-fidelity perception & planning validation, synthetic data),  +
-Rosbag Replay (real-world perception regression),  +
-Scenario Simulator v2 (scenario-based planning validation),  +
-CommonRoad (planning benchmarking),  +
-SUMO (traffic co-simulation) +
-Scenic (Scenario generator for CARLA) +
-TalTech +
-#2 +
-F1Tenth +
-The F1TENTH use case demonstrates the application of open-source V&V frameworks for validating autonomous driving functions on a 1/10-scale research platform. Verification focuses on trajectory tracking, ensuring that the vehicle follows safe and stable paths without collisions or oscillations when encountering static or dynamic obstacles. In addition, the setup supports perception-level validation, including object and traffic sign detection, persistence of observations, and accuracy of estimated object velocities. Sensor data are also subject to pre-processing validation, guaranteeing their reliability for mapping, localization, and higher-level decision-making. This use case highlights how compact, reproducible, and ROS2-based V&V environments can provide students and researchers with an accessible platform for hands-on testing of autonomous vehicle algorithms and safety assurance methods. +
-Rosbag Replay (real-world perception), +
-F1TENTH Gym (simple 2D simulation) +
-ROSMonitoring (verifying at runtime) +
-RoboFuzz (testing with noisy inputs) +
-CTU +
-#3 +
-Mobile robot +
-The V&V framework for the cooperative indoor logistics robot system ensure that V&V are systematically applied across design, simulation, and real-world operation. Verification focuses on requirement traceability, component and interface testing (ROS2 nodes, MQTT messaging, sensors, DL segmentation, and planners), and formal checks for safety and deadlock freedom. Validation uses simulation, hardware-in-the-loop, and on-site trials to confirm that the system performs safely and efficiently under realistic conditions, including communication losses and operator overrides. Runtime monitoring, safety shields, and continuous testing in the CI/CD pipeline maintain ongoing assurance, ensuring reliable autonomous operation and operator control in dynamic indoor logistics environments. +
-Nav2 and MoveIt 2 (navigation and motion planning) +
-Gazebo/Ignition (simulation and testing)  +
-MQTT broker Eclipse Mosquitto for message exchange +
-OpenCV and PCL  vision and LIDAR processing +
-ROS2 testing tools +
-RTU +
-#4 +
-Drone +
-Simulation-based verification and validation (V&V) for unmanned aerial vehicles (UAVs) is essential before executing real-world missions to ensure software correctness, safety, and performance. The framework must simulate the drone’s plant model, environmental perception, and control system with sufficient fidelity while maintaining real-time execution capability. Both software-in-the-loop (SITL) and hardware-in-the-loop (HIL) configurations are required to validate autonomous behaviors such as navigation in complex environments, object detection, and precise landing. Modern 3D simulation engines, such as Unreal Engine used in CARLA, now enable highly realistic testing conditions—some even experimentally extended to aerial vehicles—providing a robust basis for safe, repeatable, and educational UAV validation. +
-AirSim (real-world perception) +
-Gazeboo (open source simulator)  +
-ArduPilot, QGroundControl (primary framework) +
-UAV Matlab/Simulink Toolbox (real-time simulation and deployment for UAV) +
-CARLA (Unreal Engine based simulator) +
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-===== Key Findings and Recommendations ===== +
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-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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-The selected frameworks fulfill higher-education requirements: +
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-- **Accessibility:** open-source and licence-free use. +
-- **Ease of setup:** Docker-based deployment for classroom or remote use. +
-- **Pedagogical link:** supports blended learning (MOOCs + hands-on labs). +
-- **Interdisciplinary use:** applicable in robotics, AI, safety engineering, and mechatronics courses. +
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-These outcomes directly contribute to WP3 educational digital content and WP2 modular curriculum design. +
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-1. **Autoware.Universe** should be the baseline platform for SafeAV framework adaptation. +
-2. **AWSIM/CARLA + SUMO/Scenic** provide complementary environments for digital twin, perception, and traffic-level validation. +
-3. **ROS-based verification tools** enable node-level and formal validation, aligning with ISO 26262 / SOTIF principles. +
-4. Partner use cases ensure coverage of ground, aerial, and hybrid autonomous systems for educational demonstration. +
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-===== Next Steps → T4.2 Adaptation ===== +
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-- Containerize selected frameworks for student deployment. +
-- Develop a hands-on educational guide linking WP3 digital content to WP4 V&V examples. +
-- Integrate simulation exercises into the SafeAV MOOC platform. +
-- Define data interfaces (ROS bag, OpenSCENARIO) for cross-use of materials. +
-- Establish KPIs for student learning outcomes (practical validation success, reproducibility, safety comprehension). +
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en/safeav/handson/intro.txt · Last modified: by raivo.sell
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