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An IMPC Based Parking Assistance System

Journal Article
2019-01-2614
ISSN: 2641-9637, e-ISSN: 2641-9645
Published October 22, 2019 by SAE International in United States
An IMPC Based Parking Assistance System
Sector:
Citation: Ouyang, Q. and Jia, X., "An IMPC Based Parking Assistance System," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(2):955-965, 2020, https://doi.org/10.4271/2019-01-2614.
Language: English

Abstract:

This paper summarizes progress and outcome from our research projects on adaptive model predictive control based parking management system, including parking motion planning and control strategy, as well as a localization strategy for parking spot. IMPC here refers to interactive model predictive control regime, which is characterized in that multiple agents implementing separate MPC strategy are incorporating information about their state, objective, and constraints. To predict future parking parameters, we proposed a practical framework which integrates anticipatory techniques with a model predictive approach that robustly models the uncertainty intrinsic of parking environment. The control regime puts comprehensive consideration into the interactions between vehicle subsystems, thus capable of optimizing trajectory under complex input disturbances under real-world driving scenarios. Moreover, Adaptive model predictive control is utilized to optimally minimize a cost function regarding time elapse, peripheral convenience and vehicle dynamics correspondent with surrounding vehicle states. The objective function is solved by dynamic programming under calibrated constraints in terms of velocity and steering accuracy, which yielded superior performance in comparison with convex programming. A dedicated guidance system was developed for navigating user to the parking spot for ease of locating parking location, which is characterized in that swift location and path are generated by BLE-based sensor fusion. Upon parking action finalization, the BLE sensor transmits the parking location to preferred communication equipment of user, which serves as goal of parking retrieve assignment. Simulation results show promising expected cost minimization in typical parking environments under consideration of numbers of trials, parking time and distance to destination. The achieved accuracy of MPC-led parking management strategy is able to improve the precision up to 0.3 meter, within the simulated highly disorganized parking environment. Meanwhile, the state of art park spot search module is able to shorten the time for drivers to locate their vehicle with positioning error of less than 1.2 meter.