Scenario-Based Validation for Outdoor Logistics and Yard Automation

SynDAB provides a comprehensive framework for simulating logistics yard environments and generating synthetic sensor data for training autonomous material handling systems. The system supports scenario-based development and traceable performance evaluation for AI modules.

• Realistic simulation of complex outdoor logistics layouts
• Reduces field testing costs and improves safety
• Configurable object types, movement patterns, and environmental conditions
• Supports seamless integration with AGVs, autonomous forklifts, and trailer-mounted AI systems

• Port and container terminal automation
• AI validation for yard tractors and outdoor delivery robots
• Robotics in industrial logistics zones

Autonomous vehicles in logistics settings, such as ports, warehouses, and industrial yards, must operate in semi-structured, cluttered environments where human-robot interaction, weather, and surface variability pose significant risks. Gathering representative training data across the full spectrum of use cases is not only expensive, but also practically infeasible. Furthermore, certification for logistics robotics (e.g., under IEC 61508 or ISO 3691-4) increasingly demands evidence of system robustness through structured and traceable test scenarios.

The invention originates from the Institute of Industrial Automation and Software Engineering (IAS) at the University of Stuttgart, led by Prof. Michael Weyrich (Institute Director) and Prof. Christof Ebert. Their research focuses on AI-based testing, validation, and certification of autonomous and safety-critical systems operating in complex, semi-structured, or safety-sensitive environments.

This technology extends their validated simulation-driven framework to logistics operations such as ports, yards, and industrial sites. It enables high-fidelity scenario generation for validating AI algorithms and safety systems, where traditional data collection and testing are logistically difficult, expensive, or unsafe.

Validating autonomous logistics systems requires exposure to diverse operational scenarios across ports, warehouses, and distribution yards. Collecting sufficient real-world data is time-intensive and often fails to capture key safety risks such as unpredictable human-machine interaction or system failure at loading points.
Moreover, current testing methods are difficult to scale or repeat, limiting their effectiveness in meeting functional safety and regulatory validation requirements. There is a critical need for synthetic, scenario-based validation tools to reduce development time while ensuring robust system performance.

For logistics operations in ports, warehouses, and industrial yards, SynDAB provides a virtualized test framework where synthetic data simulates real-time operations including loading dock interactions, dynamic human presence, and environmental factors such as rain or fog.

The AI-driven scenario engine identifies high-risk logistics use cases and produces a Minimum Viable Test Set (MVTS) for targeted validation of perception, decision-making, and navigation algorithms. This approach drastically reduces the number of test scenes required while maintaining broad risk coverage.

The system supports industry standards such as ISO 3691-4 (driverless industrial trucks), IEC 61508, and ISO/TS 15066 (collaborative robots), and can be used to demonstrate conformity with workplace safety regulations. Integration with logistics simulation platforms and QA systems is supported.

The technology can be commercialized through licensing, offering direct integration into logistics simulation, QA, and certification workflows. In addition, the inventors’ team at the University of Stuttgart provides cooperation or consulting projects to tailor the existing SynDAB software modules for specific logistics automation scenarios—such as AGV fleets, autonomous yard tractors, and mixed human-robot operations.

Ebert, C. et al. (2023). AI-Based Testing for Autonomous Vehicles. ResearchGate link

IAS (2022). Testing Software Systems. University of Stuttgart. Link

Fickinger, M. et al. (2023). Simulation-based Validation for Construction Robotics. Automatisierungstechnik, De Gruyter. Link