High-definition (HD) maps are becoming essential for Advanced Driver Assistance Systems and Fully Self-Driving technology. Given the vast scale and dynamic nature of road networks, crowdsensing appears to be the only viable solution for maintaining these maps. However, sustaining user contributions requires effective incentive mechanisms. In this work, we analyze such a system using game theory, nonlinear programming, and deep learning. Specifically, we employ a Stackelberg evolutionary game framework and optimize incentive strategies using a Deep Deterministic Policy Gradient method enhanced with Evolutionary Algorithms. Our recent theoretical results highlight the necessity of incentives to ensure sustained participation, while also revealing challenges in rural areas due to participation fluctuations, which may impact long-term data reliability.