DA-WAM: Decision-Aligned Future Latents for Driving World Models
DA-WAM proposes a decision-aligned world action model that links future predictions directly to trajectory selection. It combines a Joint-Embedding Predictive Architecture with candidate-level scoring, where action-conditioned predictors generate distinct future latent states for each possible path. This ensures the model learns representations that capture safety and utility relevant to driving decisions.