Design, development and manufacture of motor vehicles, towed vehicles, motorcycles and mopeds.
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The objective of this research is to create tools to manage uncertainty in the design and certification process of safety-critical aviation systems. The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components. These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations.
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Objective: The objective of this project is to improve the performance and current capabilities of automotive active safety control systems by taking into account the interactions between the driver, the vehicle, the active safety system and the environment. The current approach in the design of automotive active safety systems follows the philosophy "one size fits all," in the sense that active safety systems are the same for all vehicles and do not take into account the skills, habits and state of the human driver who may operate the vehicle.
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Traffic control management strategies have been largely focused on improving vehicular traffic flows on highways and freeways but arterials have not been used properly and pedestrians are mostly ignored. New urban arterial designs encourage modal shifts which gives further impetus to devise novel traffic control strategies to more quickly respond to changing conditions and salient events, while balancing safety and efficiency for all users.
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Modern cyber-physical systems are monitored and controlled by multi-core platforms, and thermal management of multi-core chips is critical as overheated cores thereon will suffer from exponentially decaying lifetime and unacceptable performance degradation. To meet the timing and system lifetime reliability requirements under dynamic workloads and operating environment, we need a real-time thermal management (RTM) scheme that predicts run-time temperature and actuates effective thermal control without compromising task deadlines.
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Recent progress in battery technology has made it possible to use batteries to power various physical platforms, such as ground/air/water vehicles. These platforms require hundreds/thousands of battery cells to meet their power and energy needs. Of these, automobiles, locomotives, and unmanned air vehicles (UAVs) face the most stringent environmental challenges. In particular, and of special importance to the automotive industry, is the transition from conventional powertrains to (plug-in) hybrid and electric vehicles, all of which are subject to environmental and operational variations.
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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing
environmental conditions. However, cost-effective deployments of such capabilities have remained
elusive. Such deployments will require software infrastructure that enables multiple sensor-processing
streams to be multiplexed onto a common hardware platform at reasonable cost, as well as tools and
methods for validating that required processing rates can be maintained.
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Security and privacy concerns in the increasingly interconnected world are receiving much attention from the research community, policymakers, and general public. However, much of the recent and on-going efforts concentrate on privacy in communication and social interactions. The advent of cyber-physical systems, which aim at tight integration between distributed computational intelligence, communication networks, physical world, and human actors, opens new possibilities for developing intelligent systems with new capabilities.
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The current lack of toolchain for high confidence testing, validation and verification of advanced, connected and automated/autonomous vehicles can impede and even entirely prevent the introduction of such vehicles into mass production. To address this challenge, this projects develops theory, methods, and tools for generating and optimizing test trajectories and data inputs that can maximize opportunities to uncover faults in both physical and cyber domain in future automotive vehicles.
