Design, development and manufacture of motor vehicles, towed vehicles, motorcycles and mopeds.
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Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with lineofsight depth perception sensors like 3D cameras. These 3D sensors are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions.
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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, we are proposing a novel approach to collect data from a sensor-equipped vehicle. Motion Sensors (Inertial Measurement Units) are placed on various locations in the car, particularly around the driver's operational environment and moving car components, such as steering wheel, seat, pedals, as well as critical car components (e.g. motor, suspensions).
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Our results concern using program-slicing to model software components in a CPS, with applications to verification of aviation software.
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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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Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to chang- ing 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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Automation is being increasingly introduced into every man-made system. The thrust to achieve trustworthy autonomous systems, which can attain goals independently in the presence of significant uncertainties and for long periods of time without any human intervention, has always been enticing. Significant progress has been made in the avenues of both software and hardware for meeting these objectives. However, technological challenges still exist and particularly in terms of decision making under uncertainty.
