{
  "$type": "site.standard.document",
  "description": "In various examples, a control stack may include a sequence of machine learning models (MLMs) respectively predicting a sequence of differentiable outputs to determine one or more control sequences. Disclosed approaches may be used to implement an AV stack that is differentiable and modular…",
  "path": "/patents/1377599",
  "publishedAt": "2025-12-25T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/0027",
    "NVIDIA Corporation"
  ],
  "textContent": "In various examples, a control stack may include a sequence of machine learning models (MLMs) respectively predicting a sequence of differentiable outputs to determine one or more control sequences. Disclosed approaches may be used to implement an AV stack that is differentiable and modular end-to-end-allowing for interpretability of the outputs and propagation of gradients backwards so that upstream predictions are learned with respect to downstream decision making. The disclosure provides various approaches for interfacing perception with motion prediction in a differentiable manner, as well as for interfacing motion prediction with motion planning and motion control in a differentiable manner.",
  "title": "DIFFERENTIABLE AND MODULAR END-TO-END STACKS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS"
}