Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Ahmed Hussein,Pablo Marín-Plaza,José María Armingol,Fernando García

doi:10.1155/2018/2493401

Ahmed Hussein, Pablo Marín-Plaza + Show 2 more

Open Access

https://doi.org/10.1155/2018/2493401

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Abstract

Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

Highlights

Self-driving cars could revolutionize how people get around; with the introduction of automation into roads, it will contribute to solving the issues related to the traffic accidents, congestion, and energy consumption
The authors in [35] adjusted the benchmark settings, to have all vehicles located at the depot, where the vehicles are required to visit all locations in the Processor Memory Storage
The Multirobot Systems (MRS) has received a significant consideration from various researchers in the Intelligent Transportation Systems (ITS) field

Summary

Introduction

Self-driving cars could revolutionize how people get around; with the introduction of automation into roads, it will contribute to solving the issues related to the traffic accidents, congestion, and energy consumption. The driverless vehicle technologies are advancing on a great scale, the multiple sensors fusion techniques, deep learning, and computational intelligence. Together, they enable these vehicles to understand the nearby surroundings and take appropriate actions to navigate on their own from one point to another. During the last decade, Multirobot Systems (MRS) fell under the research attention of the ITS community. This increased interest comes from the significant advantages and higher potential provided by MRS over single robot systems. With reference to the literature review survey, the coordination and cooperation among multiagent systems can be modeled as Multirobot Task Allocation (MRTA) problem. The decision of which robot will do which task strongly affects the performance of the system [9, 10]

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