Autonomous Decision Making for Air Taxi Networks

TL;DR

This paper introduces a novel decision-making framework for autonomous air taxi networks, enhancing safety and efficiency through optimized vehicle assignment, flight level planning, and conflict resolution.

Contribution

It presents a three-phase decision model and a simulator for the air traffic network problem, improving safety and reducing passenger wait times in urban air mobility.

Findings

Increased safety compared to baseline protocols.

Reduced passenger waiting times.

Effective conflict resolution in simulated environments.

Abstract

Future urban air mobility systems are expected to be operated by rideshare companies as fleets, which will require fully autonomous air traffic control systems and an order of magnitude increase in airspace capacity. Such a system must not only be safe, but also highly responsive to customer demand. This paper proposes the air traffic network problem (ATNP), which models the optimization problem of future cooperative air taxi networks. We propose a three-phase decision making model that efficiently assigns vehicles to passengers, determines flight levels to reduce collision risk, and resolves aircraft conflicts by selectively applying Monte Carlo tree search. We develop a simulator for the ATNP and show that our approach has increased safety and reduced passenger waiting time compared to greedy and first-dispatch protocols over potential vertiport layouts across the Bay Area and New York City.