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- W4300981504 abstract "In a common parking lot, much of the space is devoted to lanes. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. However, the advent of autonomous vehicles gives us an opportunity to overcome this constraint, and to achieve a higher storage capacity of cars. Taking advantage of self-parking and intelligent communication systems of autonomous vehicles, we propose puzzle-based parking, a high-density design for a parking lot. We introduce a novel method of vehicle parking, which leads to maximum parking density. We then propose a heuristic method to solve larger problems, and mathematically prove that the method produces near-optimal results. To improve layout designs reducing vehicular movements, we propose a use of a meta-heuristic algorithm integrated with a deep reinforcement learning method. Finally, to take advantage of these puzzle-based designs in large-scale, we propose a modular layout design. This design process consists of two steps: i) design of a high-density modular lot, which we call sub-lot, and ii) integration of these sub-lots into a large parking lot. We have conducted a set of experiments to determine which sub-lot size provide the best performance in terms of density and retrieval time." @default.
- W4300981504 created "2022-10-04" @default.
- W4300981504 creator A5005346043 @default.
- W4300981504 date "2022-10-04" @default.
- W4300981504 modified "2023-09-26" @default.
- W4300981504 title "High-density parking for autonomous vehicles." @default.
- W4300981504 doi "https://doi.org/10.18297/etd/3677" @default.
- W4300981504 hasPublicationYear "2022" @default.
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