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Connectivity and artificial intelligence will be major features of many upcoming products. The need for accurate estimation of the state of these products and their operational environments, and, the intricacy of their decision, planning, and control algorithms, will cause unprecedented growth in their design complexity as well as their software content. The failures of complex software-intensive electronically controlled products of today can often be traced to the interfaces between different subsystems and to the intersection between different engineering disciplines, i.e., mechanical, electrical, and software. Experts who possess intuition regarding the failure modes and robust design of complex electronically controlled products are few and consequently, information management solutions that can help capture and reuse the product failure modes are crucial for delivering dependable, software-intensive products.

Cars and trucks today are getting fitted with a large number of sensors in an effort to improve safety, comfort, fuel economy and emissions. The revenue from the automotive sensors market, driven by intense global competition and regulation, is expected to double over the next decade, while the size of the automotive sensors market, over the same period, is expected to triple. The field of sensor-fusion is highly multi-disciplinary, making use of technics from artificial intelligence, pattern recognition, digital signal processing, control theory, and statistical estimation. Sensor-fusion strategies based on probability theory, evidence theory, fuzzy theory, and possibility theory are being explored in different industries, e.g., defense, robotics, automotive, etc. The majority of sensor-fusion operators are based on optimistic assumptions about reliability of the information generated by the sensors.