We present a comparative study of methods for generating realistic, constrained small- to medium-scale road networks with built-in redundancy. In this research, we evaluate the proposed Evolutionary Algorithm (EA) with connectivity and redundancy constraints against the Wave Function Collapse (WFC) method - commonly used in procedural terrain generation for games - and swarm algorithms: Particle Swarm (PSO) and Gray Wolf (GWO). Our focus is on producing realistic, redundant road networks suitable for vision, localization and navigation problems. We evaluate metrics: connectivity, cycles, intersections, dead ends, graph cut-edges while enforcing physical plausibility. We propose an EA and its extended version with elitism via MAP-Elites method. We detail the implementation, constraints, metrics and provide both visual and quantitative comparisons with baselines. Results highlight how fitness function design choices affect the structural characteristics of generated networks and highlight the impact of specific constraints in practical applications. Our contribution is a method for creating realistic synthetic datasets from sparse tile definitions derived from real-world data. We demonstrate a practical application by generating realistic maps using a laboratory-collected tileset from a Duckietown city model. Our approach performs coherent geometric transformations on metadata, in this work exemplified by semantic segmentation masks of the generated road networks.

翻译：本文对生成具有内置冗余度的逼真、受限中小规模道路网络的方法进行了比较研究。我们评估了所提出的具有连通性和冗余度约束的进化算法（EA），并将其与常用于游戏程序化地形生成的波函数坍缩（WFC）方法以及群智能算法——粒子群算法（PSO）和灰狼算法（GWO）进行了对比。研究聚焦于生成适用于视觉、定位和导航问题的逼真冗余道路网络。我们评估了连通性、环数、交叉口、死胡同、图割边等指标，同时确保了物理可行性。我们提出了基础型EA及其基于MAP-Elites精英策略的扩展版本。详细阐述了算法实现、约束条件、评价指标，并提供了与基线方法的可视化及定量对比。结果表明适应度函数设计选择如何影响生成网络的结构特征，并揭示特定约束在实际应用中的影响。我们的贡献在于提供了一种从真实世界数据衍生的稀疏图块定义中创建逼真合成数据集的方法。通过使用实验室从Duckietown城市模型采集的图块集生成逼真地图，我们展示了该方法的实际应用。本方法可对元数据（本文以生成道路网络的语义分割掩码为例）执行一致的几何变换。