Radar Technologies for Safe and Efficient Transportation Systems

Intelligent mobility and transportation systems require reliable acquisition and interpretation of environmental information. Radar‑based and multimodal sensor systems enable robust perception even in fog, rain, or steam, thereby forming the foundation for safe and efficient mobility solutions in the air, on roads, on rail, and on inland waterways. These systems are complemented by AI‑supported signal processing and Joint Communication and Sensing (JCAS) for connected and cooperative applications. This enables solutions for precise localization, robust tracking, and intelligent path planning, extending to assistance and safety functions. Simulation environments and digital twins support the development and validation of complex scenarios under realistic conditions.

Further Information: JCAS - Joint Communication and Sensing for Next-Generation Wireless Networks

If you aim to make your mobility and transportation systems safer, more efficient, and future‑ready, we support you with tailored solutions based on radar, sensor fusion, and AI technologies.

Applications in Airspace

Multimodal sensor concepts and high‑resolution radar systems enable reliable monitoring of airspace, particularly at low altitudes. Aerial objects such as drones, UAVs, and air taxis are detected, tracked, and classified in real time. This supports safe flight operations, collision avoidance, and precise landing maneuvers.

Both stationary and airborne systems are used to ensure continuous sensing of the environment and other air‑traffic participants. Advanced signal processing and micro‑Doppler analysis enhance system robustness in the context of Urban Air Mobility. Adaptive sensor concepts dynamically adjust to changing environmental conditions, ensuring reliable perception. Our systems are extensively tested and validated in demonstrators, real‑world measurement campaigns, and simulation scenarios.

Applications for Land‑Based Transportation Scenarios

With long‑standing experience in autonomous driving, we develop technologies for safe, automated, and connected mobility in road traffic. Real‑time signal processing, sensor fusion, and environmental perception ensure reliable operation even in complex traffic situations. These are complemented by modern signal and waveform concepts that support interference‑resistant communication and robust sensor data acquisition.

Multimodal simulation environments such as SCaRL enable realistic rapid prototyping without extensive measurement campaigns, accelerating the development and evaluation of new algorithms and sensor configurations. The developed solutions are also applied in urban rail transport, particularly for 3D environmental sensing, object detection, and safe navigation of automated vehicles.

Further Information SCaRL - Synthetic Multi-Modal Dataset for Autonomous Driving

Applications in Maritime Transportation

Dense inland shipping and operations under fog or extreme weather conditions impose high demands on safe navigation and reliable environmental perception. Enhanced methods for detection, tracking, and path planning help prevent collisions with vessels and other obstacles while improving operational efficiency.

Distributed sensors on board and along the shoreline, combined with synchronized data processing, create a comprehensive situational picture of the environment. This enables reliable navigation, safe coordination in vessel traffic, and supportive or partially automated functions. These systems are complemented by visualization solutions and additional sensors such as cameras. The developed approaches are tested under real‑world conditions using commercial maritime radar systems.

Applications of JCAS & 6G for Transportation Systems

Joint Communication and Sensing (JCAS) in the context of future 6G networks enables the simultaneous use of communication and sensing signals. This allows data transmission and environmental perception within the same system, creating interconnected platforms that exchange information while perceiving their surroundings in parallel.

Combined with multimodal sensor data and AI methods, these approaches are used for imaging techniques as well as automated object and scene analysis. Synthetic data and data‑driven augmentation methods support the development of robust solutions for a wide range of mobility and logistics applications.