A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,
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UNVERDICTED 2representative citing papers
Adversarial attacks on cloud perception models plus network impairments in a vehicle-cloud loop degrade object detection from 0.73/0.68 to 0.22/0.15 precision/recall and destabilize closed-loop vehicle control.
citing papers explorer
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Driving in Corner Case: A Real-World Adversarial Closed-Loop Evaluation Platform for End-to-End Autonomous Driving
A platform using flow matching for real-world image generation and an adversarial policy creates challenging corner cases to evaluate end-to-end autonomous driving models like UniAD and VAD, showing performance degradation.
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Adversarial Robustness Analysis of Cloud-Assisted Autonomous Driving Systems
Adversarial attacks on cloud perception models plus network impairments in a vehicle-cloud loop degrade object detection from 0.73/0.68 to 0.22/0.15 precision/recall and destabilize closed-loop vehicle control.