Smart Routing: A Novel Application of Collaborative Path-Finding to Smart Parking Systems

Rhodes, Callum; Blewitt, William; Sharp, Craig; Ushaw, Gary; Morgan, Graham

doi:10.1109/cbi.2014.22

Cited by 41 publications

(14 citation statements)

References 13 publications

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“…Modeling and simulation have been used for knowledge discovery applicable to parking systems, as well as for testing already implemented SPS. Some examples of the first approach are: testing a utility function that involves factors affecting parking choice [4], collaborative path-finding in a multi-agent context applied to an SPS [12], testing a parking planning algorithm [13,14], and an SPS evaluation considering several vehicle categories [15]. Examples of the second approach include: testing an SPS model to explore factors like distance to building entrances [16], testing dynamic prices assignment [5], and parking guidance evaluation [6].…”

Section: Smart Parking Systems and Parking Simulationsmentioning

confidence: 99%

An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations

Mendoza-Silva

Gould

Montoliu

et al. 2019

IJGI

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This paper presents the development of a parking occupancy simulator to support a smart parking system. The simulator uses an agent-based approach to model drivers who follow activity plans and who may or may not use the smart parking system. We illustrate how the process of developing our simulator helped in the design and implementation of the smart parking system components. The paper also shows how the simulator was used to study the possible usage of the smart parking system in a university campus, foreseeing (1) support for the smart parking system’s overall suitability, (2) reservation guarantee violation problems, and (3) the value of using total traveled distance as a metric for the smart parking evaluation. The experience presented in this paper may prove valuable to teams planning the development of a smart parking system for similar contexts.

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Section: Smart Parking Systems and Parking Simulationsmentioning

confidence: 99%

An Occupancy Simulator for a Smart Parking System: Developmental Design and Experimental Considerations

Mendoza-Silva

Gould

Montoliu

et al. 2019

IJGI

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“…Constraints imposed due to the terrain make the translation of such algorithms for ground vehicles inefficient. Algorithms have been proposed for collaborative path finding for autonomous vehicles [17][18][19][20]. For many critical missions like transportation of military cargo, personnel and mined ore, it is desirable that the vehicles are commandeered real time by humans.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles

Roy

Wan²,

Goswami

et al. 2018

Journal of Dynamic Systems, Measurement, and Control

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A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.

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“…The principal model scale reproductions of the framework have been examined by them notwithstanding the calculated engineering of IPA. Callum Rhodes [6] proposed thefinding of path so that efficiency is improved to the smart parking systems and also helps in reducing traffic congestion in cities. Now a lot of cities have suitable parking system to eliminate the traffic.…”

Section: Energy Engagementmentioning

confidence: 99%