# Interval Prediction for Continuous-Time Systems with Parametric Uncertainties

arXiv: https://arxiv.org/abs/1904.04727 · HTML: https://mlsanity.com/paper/1904.04727/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge](https://mlsanity.com/paper/2103.10245/) (2021)

## Abstract (arXiv)

The problem of behaviour prediction for linear parameter-varying systems is considered in the interval framework. It is assumed that the system is subject to uncertain inputs and the vector of scheduling parameters is unmeasurable, but all uncertainties take values in a given admissible set. Then an interval predictor is designed and its stability is guaranteed applying Lyapunov function with a novel structure. The conditions of stability are formulated in the form of linear matrix inequalities. Efficiency of the theoretical results is demonstrated in the application to safe motion planning for autonomous vehicles.