Automotive perception stacks already have tracking, filtering, sensor fusion, and model-level robustness. But road shock and sensor vibration can still degrade lane-line stability, object boxes, and feature tracks.

The TightLoop automotive perception demo treats those failures as residual motion around an existing perception pipeline. It does not claim to classify objects better. It asks whether stabilizing the input or output residuals can improve the consistency of downstream perception signals.

This fits the broader TightLoop pattern: keep the main model or controller in place, then assist the residual behavior that remains under disturbance.

Useful metrics would include feature-track lifetime, box jitter, lane-line displacement, false track churn, and timing. A proper customer evaluation would replay recorded road-shock traces through the existing pipeline and compare the final perception stability.

The demo is therefore a hypothesis generator. The product question is whether residual stabilization improves perception enough to justify an integration step.