Archive of Accepted Position Papers
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In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the over-all vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose,
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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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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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Car and airplane designers each have lessons that they can teach the other. This paper concentrates on what we think automotive designers can learn from aircraft designers, with a short note on the converse. We will also consider some key issues that both disciplines need to work on.
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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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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.
