# Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees

arXiv: https://arxiv.org/abs/2405.10875 · HTML: https://mlsanity.com/paper/2405.10875/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 16 of 16)

- [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [Safe Planning in Dynamic Environments using Conformal Prediction](https://mlsanity.com/paper/2210.10254/) (2022)
- [Recent Advances in Recurrent Neural Networks](https://mlsanity.com/paper/1801.01078/) (2017)
- [Adaptive Conformal Prediction for Motion Planning among Dynamic Agents](https://mlsanity.com/paper/2212.00278/) (2022)
- [Sample-Efficient Safety Assurances using Conformal Prediction](https://mlsanity.com/paper/2109.14082/) (2021)
- [Conformal Prediction for STL Runtime Verification](https://mlsanity.com/paper/2211.01539/) (2022)
- [Probabilistically Safe Robot Planning with Confidence-Based Human Predictions](https://mlsanity.com/paper/1806.00109/) (2018)
- [Multi-Agent Reachability Calibration with Conformal Prediction](https://mlsanity.com/paper/2304.00432/) (2023)
- [Group-based Motion Prediction for Navigation in Crowded Environments](https://mlsanity.com/paper/2107.11637/) (2021)
- [Reactive motion planning with probabilistic safety guarantees](https://mlsanity.com/paper/2011.03590/) (2020)
- [Statistical Verification of Autonomous Systems using Surrogate Models and Conformal Inference](https://mlsanity.com/paper/2004.00279/) (2020)
- [Signal Temporal Logic Control Synthesis among Uncontrollable Dynamic Agents with Conformal Prediction](https://mlsanity.com/paper/2312.04242/) (2023)
- [Stochastic MPC with Multi-modal Predictions for Traffic Intersections](https://mlsanity.com/paper/2109.09792/) (2021)
- [Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach](https://mlsanity.com/paper/2203.14913/) (2022)
- [A Gaussian Process Model for Opponent Prediction in Autonomous Racing](https://mlsanity.com/paper/2204.12533/) (2022)

## Built on by (most cited first, 4 of 4)

- [Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization](https://mlsanity.com/paper/2510.16376/) (2025)
- [Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands](https://mlsanity.com/paper/2511.13911/) (2025)
- [Information-Theoretic Framework for Self-Adapting Model Predictive Controllers](https://mlsanity.com/paper/2603.01286/) (2026)
- [Predictive Semantic Safety: From Visual Physical Reasoning to Safety-Critical Control](https://mlsanity.com/paper/2609.34356/) (2026)

## Abstract (arXiv)

In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous system that interacts with uncontrollable stochastic agents during its mission. Employing tools from conformal prediction, existing works derive high-confidence prediction regions for the unknown agent trajectories, and integrate these regions in the design of suitable safety constraints for MPC. Despite guaranteeing probabilistic safety of the closed-loop trajectories, these constraints do not ensure feasibility of the respective MPC schemes for the entire duration of the mission. We propose a shrinking-horizon MPC that guarantees recursive feasibility via a gradual relaxation of the safety constraints as new prediction regions become available online. This relaxation enforces the safety constraints to hold over the least restrictive prediction region from the set of all available prediction regions. In a comparative case study with the state of the art, we empirically show that our approach results in tighter prediction regions and verify recursive feasibility of our MPC scheme.