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

TL;DR

This paper compares evolutionary algorithms, wave function collapse, and swarm methods for generating realistic small-scale road networks, emphasizing constraints like connectivity and redundancy to improve navigation and localization applications.

Contribution

It introduces an evolutionary algorithm with constraints for realistic road network generation and demonstrates its effectiveness with real-world data and practical applications.

Findings

EA with constraints produces realistic, connected networks

Fitness function design significantly influences network structure

The method effectively generates maps from sparse real-world data

Abstract

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.