To realize a path keeping system (PKS), it is essential to guide autonomous vehicle (AV) to a desired direction while maintaining safety. To maintain a desired driving path, it is necessary for an AV to perceive its surrounding environment by gathering directional information while traveling. Therefore, precise localization of an AV and autonomous guidance are essential technologies for the successful implementation of a PKS. As a type of radio guidance, an AV can use trilateration to identify its location relative to three reference positions by measuring the strength, time, or time difference of the reception of transmitted radio signals. Direction of arrival (DOA) techniques can also be used to estimate the direction of a signal emitted from a transmitter, and this information can be used to set the driving direction of an AV. However, in a practical environment, estimation errors may arise when the AV estimates its location or the direction of signal emitted from a transmitter, and these errors may cause the problem of path departure. To address this problem, we conducted a mathematical analysis to identify potential errors in estimating location and direction. Herein we propose a target position decision algorithm for PKS, which uses trilateration and DOA (PKS-TnD) to drive an AV in a desired direction to avoid path departure. By using this algorithm, an AV calculates its next target position based on the error constraints of trilateration and DOA by means of numerical analysis. Additionally, an energy efficiency problem is also formulated herein and energy use is optimized by using a Lagrangian equation.
Bibliographical noteFunding Information:
This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center) support program (NIPA-2014-H0401-14-1006) supervised by the NIPA (National IT Industry Promotion Agency). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2A10011764).
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering