Design, development and manufacture of motor vehicles, towed vehicles, motorcycles and mopeds.
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Abstract:
Until now, the "cyber" component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety (PERCS) goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved.
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Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers' choices of modes, locations, and time of travel. The advent of smart sensors, wireless communications, social media and big data analytics offers a unique opportunity to tap parking's influence on travel to make the transportation system more efficient, cleaner, and more resilient.
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The objective of this project is to develop a science of integration for cyber-physical systems (CPS). The proposed research program has three focus areas: (1) foundations, (2) tools and tool architectures, (3) systems/experimental research. The project has pushed along several frontiers towards these overall objectives. In the following, we describe selected accomplishments:
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The goal of this project is to develop fundamental theory, computationally efficient algorithms, and real-world experiments for the analysis and design of safety-critical cyber-physical transportation systems with human operators. To this end, we propose a modeling, theoretical, and experimental collaborative effort combining human factors, control theory, and computer science. As crashes at traffic intersections account for about 40% of overall vehicle crashes, we will focus on intersection crashes in this project.
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In order to improve the current capabilities of automotive active safety control systems (ASCS) one needs to take into account the interactions between driver/vehicle/ASCS/environment. To achieve this goal, this research will infer longterm and short-term driver behavior via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs.
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Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. While sophisticated data analysis and synthesis can be well supported in large data centers, future intelligent systems require on-the-scene processing with faster responses and less dependence on the unreliable (often wireless) data communications in the field.
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Project Description
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The objective of this research is to design a semi-automated, efficient, and secure emergency response system to reduce the time it takes emergency vehicles to reach their destinations, while increasing the safety of non-emergency vehicles and emergency vehicles alike. Providing route and maneuver guidance to emergency vehicles and non-emergency vehicles will make emergency travel safer and enable police and other first responders to reach and transport those in need, in less time.
