Towards Dynamic Pricing Based Autonomous Valet Parking Management Under Demand Uncertainty
Abstract: Benefited from the development of intelligent transportation system and autonomous vehicle, the autonomous valet parking has been attracting attentions worldwide. Compared with short-range autonomous valet parking, long-range autonomous valet parking further alleviates users from parking during long-range traveling. Although autonomous vehicles can finish parking autonomously under long-range autonomous valet parking, they still need to estimate parking cost of each parking lot and decide which one to park. However, existing works on parking cost estimation do not consider the competition with uncertain parking demand, leading to inaccurate cost estimation. Besides, fixed parking prices intensify parking competition and cause local congestion at parking lot. Therefore, we propose a Long-Range Autonomous Valet Parking Considering Uncertain Demand and Parking Pricing. Based on the uncertain demand analysis, we predict the future occupancy of parking lot and adjust its price dynamically, so as to balance parking demand. Moreover, the accurate estimation of parking cost is achieved through mathematical analysis on uncertain demand, with vision that autonomous vehicles can select parking lot with lower parking cost. Simulation results show that compared with other schemes, our scheme can outperform on reducing parking cost and balancing parking demand.
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