Generate Realistic Test Scenes for V2X Communication Systems
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Accurately perceiving complex driving environments is essential for ensuring
the safe operation of autonomous vehicles. With the tremendous progress in deep
learning and communication technologies, cooperative perception with
Vehicle-to-Everything (V2X) technologies has emerged as a solution to overcome
the limitations of single-agent perception systems in perceiving distant
objects and occlusions. Despite the considerable advancements, V2X cooperative
perception systems require thorough testing and continuous enhancement of
system performance. Given that V2X driving scenes entail intricate
communications with multiple vehicles across various geographic locations,
creating V2X test scenes for these systems poses a significant challenge.
Moreover, current testing methodologies rely on manual data collection and
labeling, which are both time-consuming and costly.
In this paper, we design and implement V2XGen, an automated testing
generation tool for V2X cooperative perception systems. V2XGen utilizes a
high-fidelity approach to generate realistic cooperative object instances and
strategically place them within the background data in crucial positions.
Furthermore, V2XGen adopts a fitness-guided V2X scene generation strategy for
the transformed scene generation process and improves testing efficiency. We
conduct experiments on V2XGen using multiple cooperative perception systems
with different fusion schemes to assess its performance on various tasks. The
experimental results demonstrate that V2XGen is capable of generating realistic
test scenes and effectively detecting erroneous behaviors in different
V2X-oriented driving conditions. Furthermore, the results validate that
retraining systems under test with the generated scenes can enhance average
detection precision while reducing occlusion and long-range perception errors.
