Formal safety proofs become difficult when an autonomous car or robot takes camera images instead of a compact state vector as input. Researchers from the University of Florida and collaborating institutions address that problem by removing randomness from the generative model used to approximate the camera.

The resulting DWM paper introduces a Deterministic World Model, a latent-free neural decoder that maps physical states directly to synthetic images. Integrated with a reachability pipeline, the model produces tighter reachable sets and stronger safety-classification results than the conditional generative adversarial network, or cGAN, used as a baseline.

Why pixels overwhelm verification

Closed-loop verification tools such as Verisig, POLAR, and ReachNN propagate sets of possible states through a controller and system dynamics. At each time step, they compute every state the system could reach and check whether that set intersects an unsafe region.

These methods work best with low-dimensional inputs such as position, velocity, and angle. A camera adds thousands of correlated pixel values, while the rendering process rarely has a closed-form representation that a verifier can manipulate. Propagating bounded image sets through a deep convolutional controller also introduces approximation error at each layer. Those errors compound until the reachable set becomes too broad to support a useful certificate.

Previous work has used cGANs as learned camera surrogates. A cGAN combines the physical state with a sampled latent vector, usually drawn from a Gaussian distribution, to generate varied images. Because the latent coordinates have no direct physical interpretation, verification must assign them conservative bounds. The verifier then includes images that the physical system may never produce, enlarging every downstream action and state set.