With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stopsign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.
Abstract-Many real world optimization problems are dynamic in which the landscape is time dependent and the optimums may change over time such as dynamic economic modeling, dynamic resource scheduling and dynamic vehicle routing. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In these environments, optimization algorithms must not just find the optima but also closely track the optima's trajectory. In this paper we propose a two phased and collaborative version of Cellular PSO, named Two Phased Cellular PSO (TP-CPSO), which introduces two search phases in order to create a more efficient balance between the exploration and exploitation of the optimums. We address the weaknesses of Cellular PSO and propose some modifications and ideas to tackle them including a modified PSO update rule and an efficient local search. Moreover, the cell capacity threshold which is a key parameter of Cellular PSO is eliminated due to these modifications. To demonstrate the performance and robustness of the proposed algorithm, Moving Peaks Benchmark (MPB) has been adopted. For all the experimented dynamic environments, TP-CPSO outperformed all compared evolutionary algorithms including Cellular PSO.
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