# Multi-Agent Reachability Calibration with Conformal Prediction

arXiv: https://arxiv.org/abs/2304.00432 · HTML: https://mlsanity.com/paper/2304.00432/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2023.

## Built on by (most cited first, 25 of 25)

- [Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions](https://mlsanity.com/paper/2310.05921/) (2023)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [Conformal Temporal Logic Planning using Large Language Models](https://mlsanity.com/paper/2309.10092/) (2023)
- [Distributionally Robust Statistical Verification with Imprecise Neural Networks](https://mlsanity.com/paper/2308.14815/) (2023)
- [Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback](https://mlsanity.com/paper/2410.08852/) (2024)
- [Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy](https://mlsanity.com/paper/2309.01267/) (2023)
- [Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data](https://mlsanity.com/paper/2307.01088/) (2023)
- [How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy](https://mlsanity.com/paper/2308.12252/) (2023)
- [Max-Rank: Efficient Multiple Testing for Conformal Prediction](https://mlsanity.com/paper/2311.10900/) (2023)
- [Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis](https://mlsanity.com/paper/2602.12047/) (2026)
- [Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts](https://mlsanity.com/paper/2311.01457/) (2023)
- [Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions](https://mlsanity.com/paper/2406.07767/) (2024)
- [Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees](https://mlsanity.com/paper/2405.10875/) (2024)
- [Conformal Off-Policy Prediction for Multi-Agent Systems](https://mlsanity.com/paper/2403.16871/) (2024)
- [Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization](https://mlsanity.com/paper/2510.16376/) (2025)
- [Learning-Based Approaches to Predictive Monitoring with Conformal Statistical Guarantees](https://mlsanity.com/paper/2312.01959/) (2023)
- [Formal Safety Verification and Refinement for Generative Motion Planners via Certified Local Stabilization](https://mlsanity.com/paper/2509.19688/) (2025)
- [How Safe Will I Be Given What I Saw? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy](https://mlsanity.com/paper/2508.09346/) (2025)
- [Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning](https://mlsanity.com/paper/2511.20811/) (2025)
- [Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting](https://mlsanity.com/paper/2407.21185/) (2024)
- [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025)
- [Conformal Reachability for Safe Control in Unknown Environments](https://mlsanity.com/paper/2602.03799/) (2026)
- [Vision-Based Safe Human-Robot Collaboration with Uncertainty Guarantees](https://mlsanity.com/paper/2604.15221/) (2026)
- [Stable Multi-Step Rollouts via Uncertainty-Guided Hybrid Dynamics](https://mlsanity.com/paper/2608.16431/) (2026)
- [Multi-Agent Deferred-Decision Trajectory Optimization with Anytime-Valid Target Retention for Moving Candidates](https://mlsanity.com/paper/2610.17003/) (2026)

## Abstract (arXiv)

We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning. Given a learning-based forecasting model that predicts agents' trajectories, we introduce a method for providing probabilistic assurances on the model's prediction error with calibrated confidence intervals. Through quantile regression, conformal prediction, and reachability analysis, our method generates probabilistically safe and dynamically feasible prediction sets. We showcase their utility in certifying the safety of planning algorithms, both in simulations using actual autonomous driving data and in an experiment with Boeing vehicles.