Test-Time Adaptation of End-to-End Trajectory Planning via Simulation-based Self-Verification

Existing Test-Time Adaptation (TTA) approaches mitigate distribution shifts for classification, detection, or segmentation, and the few for end-to-end planning update only the score decoder over a fixed trajectory vocabulary. More critically, the trajectory predicted under corruption is executed without refinement or verification, although a single unsafe one can cause a safety incident. We propose Simulation-based TTA (SimTTA), a verification-driven framework that addresses two key challenges. To verify the predicted trajectory, Geometric Trajectory Refinement (GTR) expands it in the output space into candidates beyond the reach of the corrupted model. Simulation-based Self-Verification (SSV) then simulates each candidate on a scene context reconstructed from the perception outputs of the model itself, and selects the highest-scoring one as the final trajectory without ground-truth labels or training. To adapt the planning model, the Temporally-Verified Entropy (TVE) loss minimizes the prediction entropy only on the BEV regions whose predicted classes remain the same across consecutive samples. Thus, the following samples are planned and verified with a more reliable representation. Experiments across eight corruption types demonstrate that our SimTTA significantly enhances trajectory quality under such shifts. The source code will be released.