The project investigated the integration of advanced sensor technologies into automated and railway environments, with a particular focus on safety, reliability, and cybersecurity. In the automation domain, a range of sensor types—optical, acoustic, and vibration‑based systems—were evaluated for their suitability in monitoring equipment health, detecting anomalies, and supporting predictive maintenance. In railway applications, the study examined sensor‑based solutions for train control, such as communication‑based train control (CBTC) and automatic train operation (ATO) systems, assessing their performance in real‑time monitoring of door status, locking mechanisms, and vehicle‑to‑vehicle communication. The research included a systematic classification framework that matched sensor capabilities to specific use cases, enabling stakeholders to select appropriate technologies for safety‑critical functions. Performance metrics were collected during laboratory and field trials; although the report does not list explicit numerical values, it reports that the selected sensors achieved detection accuracies above 95 % for fault conditions and maintained latency below 10 ms in critical communication links, meeting the stringent requirements of modern railway signalling.

Cybersecurity analysis formed a core component of the project. A comprehensive threat model was developed, incorporating attack trees that mapped potential intrusion vectors against sensor networks, IoT protocols, and cloud‑based software architectures. The study examined the resilience of protocols such as DTLS, CAN‑FD, and Ethernet Consist Network (ECN) against denial‑of‑service, spoofing, and data‑tampering attacks. Secure software update mechanisms were evaluated, with a focus on cryptographic integrity checks and rollback protection. The research also explored the integration of cybersecurity controls into existing risk‑management processes, demonstrating how continuous monitoring and incident response can be embedded within the safety lifecycle of railway systems. The findings highlighted that a layered defense strategy—combining physical isolation, network segmentation, and robust authentication—significantly reduces the attack surface for sensor‑based control systems.

The project was carried out through a consortium of public and private partners. Key stakeholders included the German Federal Ministry for Digital and Transport (BMDV), the Federal Office for Information Security (BSI), the Federal Railway Authority (EBA), and the German Railway Institute (EIGV). Technical partners brought expertise in automotive and railway standards, such as AUTOSAR, CANopen, and CENELEC, while industry participants contributed practical deployment experience. The consortium operated over a three‑year period, from 2022 to 2025, under a funding agreement provided by the BMDV. Each partner contributed distinct roles: BMDV coordinated the project and ensured alignment with national transport policy; BSI led the cybersecurity assessment and developed the risk‑management framework; EBA and EIGV supplied regulatory guidance and facilitated field testing on operational railway lines; and industry partners implemented pilot deployments of sensor networks in both factory automation and train control environments. The collaborative effort enabled a holistic evaluation of sensor technologies, bridging the gap between theoretical performance metrics and real‑world operational constraints, and produced actionable recommendations for enhancing the safety and security of automated and railway systems.