A game theory‐based route planning approach for automated vehicle collection. (2nd March 2021)

• Record Type: Journal Article
• Authors: Hadded, Mohamed; Minet, Pascale; Lasgouttes, Jean‐Marc
• Is Part Of: Concurrency and computation. Volume 33:Number 16(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-02
• Subjects: automated vehicles -- coalition game -- game theory -- MOPSO -- multiobjective optimization -- NSGA‐II -- parking -- platooning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6246
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: We consider a shared transportation system in an urban environment where human drivers collect vehicles that are no longer being used. Each driver, also called a platoon leader, is in charge of driving collected vehicles as a platoon to bring them back to some given location (e.g., an airport, a railway station). Platoon allocation and route planning for picking up and returning automated vehicles is one of the major issues of shared transportation systems that need to be addressed. In this article, we propose a coalition game approach to compute (1) the allocation of unused vehicles to a minimal number of platoons, (2) the optimized tour of each platoon, and (3) the minimum energy consumed to collect all these vehicles. In this coalition game, the players are the parked vehicles, and the coalitions are the platoons that are formed. This game, where each player joins the coalition that maximizes its payoff, converges to a stable solution. The quality of the solution obtained is evaluated with regard to three optimization criteria and its complexity is measured by the computation time required. Simulation experiments are carried out in various configurations. They show that this approach is very efficient to solve the multiobjective optimization problem considered, since it provides the optimal number of platoons in less than a second for 300 vehicles to be collected, and considerably outperforms other well‐known optimization approaches such as multiobjective particle swarm optimization and nondominated sorting genetic algorithm.