Learning an Urban Air Mobility Encounter Model from Expert Preferences

TL;DR

This paper introduces a novel approach for modeling urban air mobility encounters by learning from expert preferences, enabling realistic trajectory generation with minimal expert input.

Contribution

It extends preference-based learning to develop encounter models for UAM, addressing data scarcity and providing a new method for realistic trajectory synthesis.

Findings

The model effectively captures expert preferences in encounter scenarios.

Two querying methods improve the efficiency of learning from experts.

Realistic encounter trajectories can be generated with limited expert time.

Abstract

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.