Archive of Accepted Position Papers
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Automotive cyber-physical systems will need to address self-parking, advanced steering control, hazardous situation recovery, limited autonomous driving, and even more complex tasks in the coming decades. Verification of the safe behavior of these tasks for multiple vehicle configurations (weight, wheelbase, front/rear/all-wheel drive, etc.) will require significant advancements in the computational theory, as well as new approaches to compose behaviors and computational constraints with hybrid control theory and system modeling.
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The demand for higher performance computing platforms has dramatically increased during the last decade due to the continuous feature enhancement process. For instance, in automotive systems new safety features like `night view assist’ and `automatic emergency breaking’ require the fusion of sensor data, video processing and real-time warnings when an obstacle is detected on the road; in the avionics domain new applications such as the helmet-mounted display systems require intensive video processing capabilities.
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In principle, best-effort technologies can be used for building each individual automotive cyber-physical system (CPS) from the ground-up, through careful design, testing, and verification. Each such undertaking, however, is technically challenging, error-prone, and expensive. Since many of these systems share common challenges, employ common design patterns, and verification principles, it is expected that generic software tools for automating design, testing, and verification can alleviate these challenges.
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Transportation sectors are today faced with grand societal challenges of accommodating an unprecedented traffic increase, while improving travel safety, comfort and convenience, fuel efficiency, environmental benefit, and stakeholders business. Commonalities are emerging in the way aerospace and automotive sectors are responding to these grand challenges.
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Despite extensive design processes, emergent behavior will still appear at run-time in dependable automotive systems. Such behavior occurs due to unexpected or unidentifieded interactions and dependencies between system components. These interactions are unidentifieded due to a disconnect between various stages of the design process. A diagnostic advisor that synthesizes data from each stage of the product lifecycle provide a more accurate design-time characterization of the system, as well as more robust run-time operation.
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The Vision: Our planet has become more urban than rural in the last decade. Urban traffic has increased dramatically, making driving more stressful, costly, and unhealthy. According to the Texas Transportation Institute, the overall cost of metropolitan traffic congestion (in terms of wasted fuel and lost economic productivity) in the U.S. topped $87 billion in 2007, more than $750/year for every U.S. traveler.
