Elite Lanes: Evolutionary Generation of Realistic Small-Scale Road Networks

2026-03-24 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

This research presents "Elite Lanes," an Evolutionary Algorithm (EA) with MAP-Elites for generating realistic, constrained small- to medium-scale road networks with built-in redundancy. The study comparatively evaluates this proposed EA against Wave Function Collapse (WFC), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO) methods. The primary goal is to produce synthetic road networks suitable for computer vision tasks like semantic segmentation, localization, and navigation, particularly in low-data scenarios. The authors define road networks using a 4-bit integer tile-based representation and enforce multiple constraints, including connectivity matching, boundary adherence, and minimizing dead ends and adjacent crossings. Evaluation metrics include connected components, cyclomatic complexity, straight road scores, and adjacent turn counts. Experiments on a 12x12 grid, using a Duckietown city model tileset, demonstrate that the MAP-Elites enhanced EA significantly outperforms baselines in solution diversity and quality, achieving 0 boundary violations, 0 dead ends, and 0 bridges in its best outputs.

Key takeaway

For AI Scientists and Research Scientists developing autonomous navigation or computer vision models, this work demonstrates that employing Evolutionary Algorithms with MAP-Elites can significantly enhance the realism and diversity of synthetic road network datasets. You should consider integrating quality-diversity optimization techniques like MAP-Elites into your procedural content generation pipelines to mitigate the "reality gap" and improve model generalization, especially when real-world data is scarce. This approach yields superior, highly constrained, and redundant network topologies compared to traditional methods.

Key insights

MAP-Elites-enhanced Evolutionary Algorithms excel at generating diverse, high-quality synthetic road networks for robotics applications.

Principles

• Explicit diversity maintenance improves search space exploration.
• Fitness function design critically impacts network structural characteristics.
• Constraint enforcement is vital for physical plausibility.

Method

The method involves representing road networks as 4-bit integer tile grids, applying an EA with MAP-Elites for quality-diversity optimization, and using a multi-objective fitness function to minimize undesirable features like dead ends and adjacent crossings while maximizing cyclomatic complexity and straight roads.

In practice

• Use MAP-Elites for diverse synthetic dataset generation.
• Define behavior descriptors to guide solution exploration.
• Incorporate constraint repair mechanisms for valid outputs.

Topics

• Evolutionary Algorithms
• MAP-Elites
• Road Network Generation
• Synthetic Data Generation
• Procedural Content Generation

Best for: AI Scientist, Research Scientist, AI Researcher, Robotics Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.