Autonomous vehicles have been at the forefront of academic and industrial research in recent decades. This study’s aim is to reduce traffic congestion, improve safety, and accidents. Path planning algorithms are one of the main elements in autonomous vehicles that make critical decisions. Motion planning methods are required when transporting passengers from one point to another. These methods have incorporated several methods such as generating the best trajectory while considering the constraints of vehicle dynamics and obstacles, searching a path to follow, and avoiding obstacles that guarantee comfort, safety, and efficiency. We suggested an effective path planning algorithm based on Model Predictive Controller that determines the maneuvers mode such as lane-keeping and lane-changing automatically. We utilized two different artificial potential field functions for the road boundary, obstacles, and lane center to ensure safety. On the four scenarios, we examined the proposed path planning controller. The obtained results show that when a path planning controller is used, the vehicle avoids colliding with obstacles and follows the rules of the road by adjusting the vehicle’s dynamics. An autonomous vehicle’s safety is ensured by the path planning controller.

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Early diagnosis is a very fundamental issue in treating most diseases and for this purpose microcantilevers are very effective and reliable devices. In this work, four models of biosensor-based microcantilever are compared and a novel design with high sensitivity, quality factor, and accuracy is proposed. A truss structure is designed near the anchored end of the microcantilever, which improves the sensitivity in order to increase detection accuracy. A linear relationship between resonance frequency shift and masses has been estimated for all the designs. High quality factor, which increases the accuracy of measurement, is taken into account as the other benefit of the proposed design. These microcantilevers can be used as an array for multiple early diagnosis of cancer and moreover these features will improve clinical applications of cantilever sensors for early disease diagnosis.

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