Model Card for Ctrl-Crash

Model Details

• Visit the project page for demos: https://anthonygosselin.github.io/Ctrl-Crash-ProjectPage/
• Visit the repository to get started: https://github.com/AnthonyGosselin/Ctrl-Crash
• Read the paper for more details: https://arxiv.org/abs/2506.00227

Uses

• (1) Crash Reconstruction: Given an initial image, full bounding box sequence, and a crash type, the model reconstructs a consistent video combining the visual context of the initial frame with agent motion derived from the bounding boxes.
• (2) Crash Prediction: Given the initial frame and only a few initial bounding box frames (e.g., 0–9), the model predicts the future motion of agents in a way that aligns with the target crash type.
• (3) Crash Counterfactuals: Extending the prediction task, this mode varies the crash type signal while keeping other inputs fixed, enabling the generation of multiple plausible outcomes for the same scene—supporting counterfactual safety reasoning.

Bias, Risks, and Limitations

• Counterfactual outcomes can be hard to generate when initial scene conditions conflict with the desired crash type.
• The model relies heavily on bounding boxes, making it sensitive to tracking errors—especially in fully conditioned reconstruction.
• With no bounding boxes conditioning, motion direction can be ambiguous, and 2D boxes struggle to capture rotation or orientation, limiting realism in behaviors like spinouts
• Does not support text conditioning