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End-to-end driving models can learn interpretable safety costs over reachable futures, drastically reducing collisions without fine-tuning.
AdvScene, a conditional latent diffusion model that is trained in two stages that Conditioning on ego driving displacement prevents the ego from remaining static, and RL finetuning induces criticality directly with non-differentiable safety-critical metrics is proposed.
