Presentation + Paper
21 April 2020 Towards cognitive vehicles: location cognizance
Author Affiliations +
Abstract
In the context of cognitive vehicles, it is essential to have full awareness of the vehicle location to have a better insight of the operational environment and enhance the driver perception. Although the global navigation satellite system (GNSS) is commonly a practical solution for vehicle localization, it may suffer quality deterioration, accuracy decay and even track loss due to signal blockage and reflection off buildings and big structures. These limitations usually manifest in big cities with dense traffic and active roads which means losing the sense of location, even for a short time, might blur the cognitive system decision making process and jeopardize the safe driving of the vehicle. Consequently, cognitive vehicles should not count only on the GNSS solution for vehicle localization. In this work we establish that the cognitive vehicle location awareness can be achieved through the inner process of interaction with the surrounding environment and observing its static reference elements. This approach is inspired by the way the human brain can assess its position in a known environment by recognizing some landmarks and referential objects. Our proposed solution allows the cognitive vehicle to ascertain its location by interacting with its surroundings. we train a deep neural network to detect some objects of reference, create a prior knowledge of the vehicle environment and estimate the vehicle location by recognizing the objects detection pattern. Finally, the proposed solution will be endorsed by promising results from a real-world scenario, and further work will be proposed to improve the solution.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abdessattar Hayouni, Mihai C. Florea, and Henry Leung "Towards cognitive vehicles: location cognizance", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141317 (21 April 2020); https://doi.org/10.1117/12.2557539
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KEYWORDS
Global Positioning System

Detection and tracking algorithms

Video

Brain

Cameras

Buildings

Error analysis

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