Challenger: Affordable Adversarial Driving Video Generation

• URL: http://arxiv.org/abs/2505.15880v2
• Date: Fri, 23 May 2025 02:48:27 GMT
• Title: Challenger: Affordable Adversarial Driving Video Generation
• Authors: Zhiyuan Xu, Bohan Li, Huan-ang Gao, Mingju Gao, Yong Chen, Ming Liu, Chenxu Yan, Hang Zhao, Shuo Feng, Hao Zhao,
• Abstract summary: Challenger is a framework that produces physically plausible yet photorealistic adversarial driving videos.<n>As tested on the nuScenes dataset, Challenger generates a diverse range of aggressive driving scenarios.
• Score: 36.949064774296076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating photorealistic driving videos has seen significant progress recently, but current methods largely focus on ordinary, non-adversarial scenarios. Meanwhile, efforts to generate adversarial driving scenarios often operate on abstract trajectory or BEV representations, falling short of delivering realistic sensor data that can truly stress-test autonomous driving (AD) systems. In this work, we introduce Challenger, a framework that produces physically plausible yet photorealistic adversarial driving videos. Generating such videos poses a fundamental challenge: it requires jointly optimizing over the space of traffic interactions and high-fidelity sensor observations. Challenger makes this affordable through two techniques: (1) a physics-aware multi-round trajectory refinement process that narrows down candidate adversarial maneuvers, and (2) a tailored trajectory scoring function that encourages realistic yet adversarial behavior while maintaining compatibility with downstream video synthesis. As tested on the nuScenes dataset, Challenger generates a diverse range of aggressive driving scenarios-including cut-ins, sudden lane changes, tailgating, and blind spot intrusions-and renders them into multiview photorealistic videos. Extensive evaluations show that these scenarios significantly increase the collision rate of state-of-the-art end-to-end AD models (UniAD, VAD, SparseDrive, and DiffusionDrive), and importantly, adversarial behaviors discovered for one model often transfer to others.

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