Design, development and manufacture of motor vehicles, towed vehicles, motorcycles and mopeds.
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The ongoing research aims to develop rules to study and methods to coordinate a network of fully and partially self-driving vehicles, interacting with conventional vehicles driven by people on a complex road grid, so that overall safety and efficiency of the traffic system can be improved.
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Automotive applications require an exceptionally high level of confidence. For instance, the faulty ignition switches in General Motors vehicles have resulted in 52 crashes and at least 2.6 million vehicles with these switches have been recalled. If each vehicle spent only 1,000 hours on the road, there has been less than 1 crash every 5 x 107 hours of operation. Test tracks and simulations are unable to reliably detect failures that occur this infrequently.
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One defining feature of cyberphysical systems is the fact that human users are closely intertwined with the physical system. Yet both the system and users themselves are often do not explicitly know how users would behave. A natural question arises: How do we design cyberphysical systems that effectively learn about their users, and optimize system behavior accordingly? This poster presents the idea of active regression as a vehicle to learn about users.
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Despite many advances in vehicle automation, much remains to be done: the best autonomous vehicle today still lags behind human drivers, and connected vehicle (V2V) and infrastructure (V2I) standards are only just emerging. In order for such cyber-physical systems to fully realize their potential, they must be capable of exploiting one of the richest and most complex abilities of humans, which we take for granted: seeing and understanding the visual world.
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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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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.
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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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This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both.
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At a high level, the goal of this project is to collect data on how drivers approach and drive through intersections. Research at the University of Michigan concerns the development of the driving simulator scenarios and testing human subjects. In addition, considerable related research is being conducted on research methods. At MIT, models based on the research at Michigan are being developed. This abstract concerns those part of the project conducted at the University of Michigan.
