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A high-speed data acquisition (DAQ) system has been designed for a time of flight PET test-bed. The requirements of the system were flexibility, data throughput and data integrity.The software is modular so that modifications and additions can be integrated easily into the existing software architecture. The program operation is driven by commands read from a script file, simplifying implementation of complex acquisition sequences. The heart of the program is the DAQ module, which efficiently transfers data from CAMAC to file. Another software module offers online or offline analysis capabilities.The software, written in LabVIEW, communicates with a novel high-speed USB2 CAMAC controller (CCUSB). The CCUSB offers significant improvements over its GPIB predecessor, supporting FIFO buffered DAQ and a variety of data readout modes. Four readout modes have been evaluated in order to maximize the DAQ rate for this particular system. A highest sustained data rate of 15.7 k events/s was achieved for approximately 60 input channels using a 22 Na flood phantom. Flexibility in the software design accommodates both current and future hardware configurations without the need to edit the LabVIEW code.

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Hazard Analysis and Risk Assessment (HARA) of ISO 26262 evaluates system malfunctions resulting in hazardous events to determine Automotive Safety Integrity Levels (ASILs). To ensure system safety, these ASILs are used to identify corresponding safety measures like fault prevention, tolerance, or mitigation, which act as safety goals to be implemented during the design time. The entire process of HARA is based on worst-case assumptions and the very premise that a human driver is available to take control of the vehicle and is thus responsible for its safe behavior. However, Autonomous Vehicles (AVs) are safety-critical systems operating in uncertain and dynamic environments envisioned to function without human attention and intervention. These worst-case assumptions prompt intensive safety measures – which are not only unnecessary for certain situations but also result in limited performance of the vehicle. Therefore, in order to ensure safety in uncertain environments and in the absence of a human driver, Dynamic Risk Management (DRM) is necessary. To this end, we propose a DRM framework that takes a risk-dependent decision for a safe reconfiguration of the system at runtime. We use data from the on-board sensors to perform risk assessment and trigger safety-actions when necessary. This aids the vehicle in making decisions that ensure its safe behavior at all possible times.