A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

Onieva, Enrique; Hernández-Jayo, Unai; Osaba, Eneko; Perallos, Asier; Zhang, Xiao

doi:10.1016/j.ins.2015.05.036

Cited by 27 publications

(18 citation statements)

References 42 publications

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“…In [33], a multi-objective evolutionary algorithm was developed to develop a fuzzy rule base for controlling the speed of the vehicles entering the intersection. The objective for optimization consists of the number of vehicles, the vehicles' position and velocity.…”

Section: B Multi Agent Approachesmentioning

confidence: 99%

A Look at Motion Planning for AVs at an Intersection

Krishnan

Aadithya

Ramakrishnan

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous Vehicles are currently being tested in a variety of scenarios. As we move towards Autonomous Vehicles, how should intersections look? To answer that question, we break down an intersection management into the different conundrums and scenarios involved in the trajectory planning and current approaches to solve them. Then, a brief analysis of current works in autonomous intersection is conducted. With a critical eye, we try to delve into the discrepancies of existing solutions while presenting some critical and important factors that have been addressed. Furthermore, open issues that have to be addressed are also emphasized. We also try to answer the question of how to benchmark intersection management algorithms by providing some factors that impact autonomous navigation at intersection.

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Section: B Multi Agent Approachesmentioning

confidence: 99%

A Look at Motion Planning for AVs at an Intersection

Krishnan

Aadithya

Ramakrishnan

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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“…Centralized V2I coordination that employs Active set method and Interior point method for optimization is used in [17]. In [18] and [19] genetic optimization of fuzzy controller are used to achieve better results. Agent based approaches are often used, e.g.…”

Section: State Of the Artmentioning

confidence: 99%

“…Since vehicles without communication capabilities cannot be excluded, [19], [22] and [23] suggest several algorithms both in roundabouts and classical intersections, based on spatio-temporal reservation technique.…”

Section: State Of the Artmentioning

confidence: 99%

Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

Bosankic¹,

Mehmedović

2016

MATEC Web Conf.

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Abstract. In this paper, a new fuzzy-behavior-based algorithm for roundabout intersection management is presented. The algorithm employs cooperative intelligence and includes intelligent vehicles and infrastructure to calculate speed profiles for different vehicles, in order to achieve more comfortable driving profiles, as well to reduce congestion and CO2 emissions. The algorithm uses adaptive spatio-temporal reservation technique and was tested in MATLAB/Simulink environment. The algorithm is designed to function in different scenarios with both cooperative and non-cooperative vehicles, as well as optional intersection infrastructure. Results have show that using the proposed algorithm different vehicle communication types can be successfully combined in order to increase traffic flow through roundabout intersections.

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“…Furthermore, Lughofer et al work on the self-adaptive evolving forecast models with incremental space updating for online prediction of micro-fluidic chip quality, where Gaidhane et al design the interval type-II fuzzy pre-compensated controller applied to robotic manipulator with variable payload [17,18]. Onieva et al suggest the multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving, and Cordón proposes the historical review of evolutionary learning methods for Mamdanitype fuzzy rule-based systems by designing interpretable genetic fuzzy systems [19,20]. Igor Škrjanc et al survey the evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification, while Sinan özbek et al design the optimal fractional fuzzy gain-scheduled predictor for a time-delay process with experimental application [21,22].…”

Section: Introductionmentioning

confidence: 99%

Interval type-II Takagi–Sugeno fuzzy-based strategy in control of autonomous systems

Mazinan

2019

SN Appl. Sci.

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The present research addresses a new control strategy with a focus on an interval type-II Takagi-Sugeno (T-S) fuzzy-based approach in the area of autonomous systems to handle a set of parameters under model uncertainties. In a word, the subject behind the research is to guarantee the desirable performance of a class of the autonomous space robot systems through the design of the aforementioned interval type-II T-S fuzzy-based control strategy, which can be considered based upon the moments of inertia, the center of mass, the profile of the thrust vector and the misalignments of the propellant engine to deal with mission operation plans, in finite burning time. Concerning the research contribution, there are no solid foundations to address the investigated strategy to deal with such a system in the literatures, and therefore the proposed three-axis fuzzy-based comprehensive control solution, in its unique form, can be of the novelty, as long as a wide range of the mission operation plans are supported. It is to address a set of parameters of the autonomous space robot systems under model uncertainties, in a synchronous manner, while the outcomes are researched in connection with the specifications of the system under control and subsequently to be analyzed with regard to a number of benchmarks to verify the acquired performance. The key core behind the proposed research is to present a solving problem through the new integration of system control for a specific space autonomous task under control, while highperformance results in correspondence with the state-of-the-art materials are acquired.

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A multi-objective evolutionary algorithm for the tuning of fuzzy rule bases for uncoordinated intersections in autonomous driving

Cited by 27 publications

References 42 publications

A Look at Motion Planning for AVs at an Intersection

A Look at Motion Planning for AVs at an Intersection

Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

Interval type-II Takagi–Sugeno fuzzy-based strategy in control of autonomous systems

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