The goal of this workshop series is to continue to define and refine the technology needs and gaps for deeply-embedded software-intensive electronic control systems that interact deeply with the physical world in ways that have stringent reliability, availability, and safety requirements
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Exhaustive state space exploration based verification of cyber-physical system designs remains a challenge despite five decades of active research into formal verification. On the other hand, models of intelligent automotive cyber-physical systems continue to grow in complexity. The testing of intelligent automotive models often uses human subjects, is expensive, and can not be performed unless the system has already been prototyped and is ready for human interaction. We propose the use of machine learning methods to learn stochastic models of human-vehicle interaction.
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The goal of any transportation system is to increase safety and efficiency of transportation infrastructure
without expanding the current infrastructure. Therefore, Intelligent Transportation System (ITS) was
received great effort in recent years targeting at applying well-established technologies in communications,
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We are at the mid-point of an NSF CPS Program supported project on Autonomous Driving in Urban Environments. The objective of this research is to “scale up” the capabilities of fully autonomous vehicles so they are capable of operating in mixed-traffic urban environments: realistic large-city driving situations with many other (mostly human-driven) vehicles. The approach is to integrate interdisciplinary advances in software, sensing and control, and modeling to address the most serious weaknesses in autonomous vehicle design revealed recently by, e.g., the DARPA Urban Challenge.
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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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Dependable and secure automotive cyber-physical systems (CPSs) are crucial as human’s lives are dependent on them. Many important subsystems in today’s automobiles such as the engine control system and the anti-brake system are hard real-time systems. If the CPUs in those systems have any fault, regardless of transient faults or hard faults, not only the computation results may be wrong, but also the results may be delivered late. Therefore, CPUs used in those systems must be able to handle two tasks: 1) detect and correct the errors, and 2) ensure that the error detection and correction can be done within the deadline so that the system can function correctly or have a grace period.
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Given the critical importance of due consideration of human factors in the design of new applications of Cyber-Transportation Systems (CTS), this position paper argues for the need for developing integrated human-in-the-loop Research, Development, Testing and Evaluation (RDT&E) facility. The paper then presents a proposed Integrated Traffic-Driving-Networking simulator which the authors are beginning to develop. This is followed by a brief description of a longer-term vision for an integrated testing facility for CTS under extreme events.
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Automobile is an important application of CPS (Cyber Physical System). However, current software development process in the automotive industry is not adequate to solve the unique problems of CPS. This paper pinpoints the limitations of the current automotive software development process in the perspective of CPS and proposes a new kernel-based approach called HW componentizing kernel as a solution.
Modeling and Verifying Intelligent Automotive Cyber-Physical Systems