RoadGen: Generating Road Scenarios for Autonomous Vehicle Testing
Fan Yang, You Lu, Bihuan Chen, Peng Qin, Xin Peng

TL;DR
RoadGen is a novel system that systematically generates diverse and complex road scenarios by connecting various parameterized components, enhancing autonomous vehicle testing with richer simulation environments.
Contribution
It introduces a method to generate diverse road scenarios with complex topology and geometry by connecting multiple parameterized components, filling a gap in existing scenario generation tools.
Findings
Successfully generates diverse road scenarios for simulation.
Demonstrates effectiveness and usefulness in autonomous vehicle testing.
Enhances diversity in topology and geometry of generated scenarios.
Abstract
With the rapid development of autonomous vehicles, there is an increasing demand for scenario-based testing to simulate diverse driving scenarios. However, as the base of any driving scenarios, road scenarios (e.g., road topology and geometry) have received little attention by the literature. Despite several advances, they either generate basic road components without a complete road network, or generate a complete road network but with simple road components. The resulting road scenarios lack diversity in both topology and geometry. To address this problem, we propose RoadGen to systematically generate diverse road scenarios. The key idea is to connect eight types of parameterized road components to form road scenarios with high diversity in topology and geometry. Our evaluation has demonstrated the effectiveness and usefulness of RoadGen in generating diverse road scenarios for…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Autonomous Vehicle Technology and Safety · Model-Driven Software Engineering Techniques
