Combining Real and Synthetic Data for Reliable Autonomous Defense Systems
Autonomous defense systems are transforming modern military operations. Whether deployed as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned surface/undersea vehicles (USV/UUVs), or other unmanned systems (UxS), these platforms rely heavily on artificial intelligence (AI), sensor fusion, and robust validation processes.
To develop reliable AI-powered autonomous systems, defense organizations are increasingly adopting data-driven development (DDD). This methodology uses large volumes of real-world and synthetic sensor data to train, test, and validate AI algorithms under realistic operational conditions.
By combining real sensor recordings with synthetic data from virtual environments, engineering teams can significantly reduce development risks, accelerate validation cycles, and improve operational readiness before deployment.
What Is Data-Driven Development?
Data-driven development (DDD) is an engineering approach that uses recorded and simulated sensor data throughout the development lifecycle of autonomous systems.
Instead of relying solely on physical testing, developers continuously collect, manage, replay, and analyze sensor data to improve perception, navigation, object detection, and autonomy functions. For autonomous defense systems, DDD enables faster development while ensuring that AI models perform reliably even in challenging environments such as:
- GPS-denied operations
- Electronic warfare conditions
- Low-visibility environments
- Complex urban scenarios
- Dynamic battlefield situations
Why Is Synthetic Data Important for Autonomous Defense Systems?
One of the biggest challenges in defense AI development is obtaining sufficient training and validation data.
Certain mission scenarios are difficult, dangerous, or prohibitively expensive to recreate in the real world. Synthetic data generated by high-fidelity simulation environments fills this gap by creating realistic sensor inputs for cameras, radar, and lidar systems.
Synthetic data helps engineering teams:
- Validate autonomous behavior safely
- Test rare edge cases
- Accelerate AI model training
- Reduce field-testing costs
- Improve system robustness
As a result, synthetic data has become a critical component in the verification and validation of modern autonomous military systems.
A Complete Tool Chain for Autonomous System Development
Successful Data-Driven Development requires more than data alone. Organizations need an integrated workflow for data acquisition, management, simulation, testing, and validation. The dSPACE ecosystem combines RTMaps, IVS, ASM, and AURELION into a scalable tool chain that supports the complete development lifecycle of automated and autonomous unmanned systems. From recording sensor data in the field to generating synthetic scenarios and executing automated replay tests, the tool chain enables continuous verification and validation while integrating seamlessly into existing CI/CD/CT workflows.
RTMaps
RTMaps (Real-Time Multisensor applications) is a high performance middleware designed to manage, synchronize, and process large volumes of heterogeneous sensor data in real time. Within a DDD workflow, RTMaps enables efficient data recording, data-fusion, software development, and replay of real world and synthetic data from cameras, radar, lidar, GNSS/INS, and vehicle or mission systems.
Its modular, graphical architecture allows development teams to rapidly prototype perception and autonomy functions, test AI algorithms with repeatable data sets, and close the loop between data acquisition and validation. RTMaps significantly reduces validation effort while ensuring determinism and traceability in mission critical unmanned defense systems.
With the new RTMaps 5 Runtime Engine that is certified by TÜV Süd according to ISO 26262 ASIL-B, the application developed using the RTMaps middleware can now be easily deployed to an embedded hardware platform.
IVS
IVS (Intempora Validation Suite) is an open, cloud-based sensor data manager with an integrated test automation framework. It provides unique methods and workflows to efficiently centralize, store, search, filter, preview, and tag recorded real world and synthetic sensor data to help you bring autonomous systems to real life faster than ever before.
IVS combines manual and automatic indexing modules with AI-based scene understanding to build a catalog of your sensor data, allowing end users to archive inefficient data segments and leverage useful ones, speeding up the development process and reducing data storage and management costs.
ASM and AURELION
ASM is an open and modular tool suite consisting of simulation models that are used in various phases of the verification and validation of control units. The models support closed-loop testing of a wide range of control algorithms and provide the corresponding dynamic controller input and output. AURELION supports early function development, scenario variation, and stress testing of AI based autonomy stacks across a wide range of operational conditions.
AURELION is a flexible software solution for physics-based sensor simulation and photorealistic visualization. It allows you to integrate realistic synthetic sensor data from cameras, lidar, and radar sensors, into your processes for developing, testing, and validating perception and driving functions.
ASM and AURELION provide synthetic data from simulated sensors to complete the dataset of recorded data from real sensors.
FAQ
What is Data-Driven Development for autonomous defense systems?
Data-Driven Development is a methodology that uses real-world and synthetic sensor data to develop, train, test, and validate AI-powered autonomous defense systems throughout their lifecycle.
Why is synthetic data used in defense AI development?
Synthetic data enables developers to test rare, dangerous, or difficult-to-reproduce scenarios safely and efficiently while reducing testing costs and accelerating validation.
Which sensors are commonly used in autonomous defense systems?
Typical sensors include cameras, lidar, radar, GNSS/INS systems, and other mission-specific sensors that provide environmental perception and situational awareness.
How can autonomous systems be validated faster?
By combining automated data management, replay testing, simulation, and synthetic data generation, organizations can significantly reduce manual testing efforts and accelerate development cycles.
Which dSPACE products support Data-Driven Development?
RTMaps supports sensor data processing, IVS manages and analyzes data, ASM provides simulation models, and AURELION generates photorealistic synthetic sensor data for testing and validation.
