# Learning an Urban Air Mobility Encounter Model from Expert Preferences

arXiv: https://arxiv.org/abs/1907.05575 · HTML: https://mlsanity.com/paper/1907.05575/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Active Preference-Based Gaussian Process Regression for Reward Learning](https://mlsanity.com/paper/2005.02575/) (2020)
- [APReL: A Library for Active Preference-based Reward Learning Algorithms](https://mlsanity.com/paper/2108.07259/) (2021)
- [Active Reward Learning from Online Preferences](https://mlsanity.com/paper/2302.13507/) (2023)
- [Preference-based Learning of Reward Function Features](https://mlsanity.com/paper/2103.02727/) (2021)
- [Learning Preferences for Interactive Autonomy](https://mlsanity.com/paper/2210.10899/) (2022)
- [Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds](https://mlsanity.com/paper/2506.21887/) (2025)
- [Active Inverse Learning in Stackelberg Trajectory Games](https://mlsanity.com/paper/2308.08017/) (2023)
- [Reactive Multi-Robot Navigation in Outdoor Environments Through Uncertainty-Aware Active Learning of Human Preference Landscape](https://mlsanity.com/paper/2409.16577/) (2024)
- [Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning](https://mlsanity.com/paper/2203.10823/) (2022)
- [Cieran: Designing Sequential Colormaps via In-Situ Active Preference Learning](https://mlsanity.com/paper/2402.15997/) (2024)
- [Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance](https://mlsanity.com/paper/2510.20916/) (2025)
- [RENEW: Towards Learning World Models and Repairing Model Exploitation from Preferences](https://mlsanity.com/paper/2607.14180/) (2026)

## Abstract (arXiv)

Airspace models have played an important role in the development and evaluation of aircraft collision avoidance systems for both manned and unmanned aircraft. As Urban Air Mobility (UAM) systems are being developed, we need new encounter models that are representative of their operational environment. Developing such models is challenging due to the lack of data on UAM behavior in the airspace. While previous encounter models for other aircraft types rely on large datasets to produce realistic trajectories, this paper presents an approach to encounter modeling that instead relies on expert knowledge. In particular, recent advances in preference-based learning are extended to tune an encounter model from expert preferences. The model takes the form of a stochastic policy for a Markov decision process (MDP) in which the reward function is learned from pairwise queries of a domain expert. We evaluate the performance of two querying methods that seek to maximize the information obtained from each query. Ultimately, we demonstrate a method for generating realistic encounter trajectories with only a few minutes of an expert's time.