A framework trains and compares MLP, transformer, and GAIL-based trajectory models on real driving data, finding that architectural differences cause large variations in robustness to PGD attacks despite similar nominal accuracy.
The evolution of criticality in deep reinforcement learning,
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Real-Time Evaluation of Autonomous Systems under Adversarial Attacks
A framework trains and compares MLP, transformer, and GAIL-based trajectory models on real driving data, finding that architectural differences cause large variations in robustness to PGD attacks despite similar nominal accuracy.