UniAda introduces a white-box multi-objective attack using adaptive weighting to generate perturbations that jointly affect steering and speed in E2E ADS, outperforming benchmarks with average deviations of 3.54-29 degrees and 11-22 km/h.
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UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
UniAda introduces a white-box multi-objective attack using adaptive weighting to generate perturbations that jointly affect steering and speed in E2E ADS, outperforming benchmarks with average deviations of 3.54-29 degrees and 11-22 km/h.