Complexity-controllable road network generation for virtual testing of autonomous driving

Complexity-controllable road network generation is pivotal for accelerated virtual simulation testing of autonomous vehicles (AVs), enabling the implementation of progressively challenging test scenarios through incrementally complex road networks and facilitating continuous evaluation of target autonomous driving algorithms to expose functional flaws and performance boundaries. To this end, this study proposes a complexity controllable road network generation approach via an optimized combination of realistic road elements. First, real-world urban road networks are decomposed into diverse road elements. Among these, elements with high potential collision risks (e.g., T-junctions, merging/diverging zones) are abstracted into parameter-configurable graph models defined by node and edge parameters. Second, the instantiation of these road element models is achieved using real-world cartographic data, followed by complexity assessment via a metric quantifying potential collision risk. Third, a complexity evaluation function for road elements is formulated to assign complexity labels to each road instance. A complexity-tunable road network optimization model is developed. Taking the realism and compactness of the generated road networks as optimization objectives, the model selects road element instances of varying complexity for optimal assembly in a nonintersecting and nonoverlapping manner, yielding virtual road networks with controllable complexity while preserving real-world characteristics. Finally, experimental validation was conducted using Xi’an cartographic data, generating 4500 virtual road networks across 15 distinct complexity levels through a combination of road elements with different complexities. To validate the effectiveness of the generated road networks, comparative virtual simulation experiments are conducted on both the generated networks and real-world road networks. Experimental results demonstrate that (1) the mean relative error between the geometric parameters of road elements in the generated networks and the expected values derived from real-world cartographic data clustering is less than 1.5%, confirming superior environmental reconstruction fidelity; (2) lane-change test scenarios constructed using the generated road networks successfully identified performance limits of the target autonomous driving algorithm and functional deficiencies under lateral approach conditions; (3) the complexity of the generated road networks shows a strong correlation (absolute correlation coefficients exceeding 0.84) with the uncomfortable driving duration and average vehicle speed in continuous free-driving scenarios, which confirms that higher-complexity road networks correspond to more rigorous testing challenges; and (4) compared to real-world road networks, the generated road networks achieve 32.4% average mileage compression when traversing an equivalent number of road elements, significantly enhancing testing efficiency.