How to Cite
Abstract
In this paper, a thorough survey on the implementation of multimodal sensor-based knowledge of environmental elements using state-of-the-art deep learning and sensor processing advancements in HAVs and FAVs with respect to the vehicle-feature gathering and determining the perception level of vehicle detectors was carried out. Although on-the-shelves sensors provide a great amount of data for developing vehicle driver assistance systems, safety, reliability, security, and economic aspects of the system come into question for the proper application of these data for the typical vehicle perception systems, especially for the HAVs and FAVs. Even the high-end sensor systems are subject to uncertainties and vulnerabilities related to the environment and the sensor itself. The decrease in the cost of sensors, increasing robustness, for example for camera sensors against lighting conditions, more advanced deep learning methodologies, and computing capabilities are the most common reasons for the implementation of top-level perception systems not only around the obstacles in any environment over deep sensor fusion but also for the anticipation of their future actions.
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