Intent-Aware MPC for Aircraft Detect-and-Avoid with Response Delay: A Comparative Study with ACAS Xu

TL;DR

This work addresses the challenge of maintaining remain-well-clear in multi-aircraft detect-and-avoid under realistic response delays. It introduces an intent-aware Model Predictive Control framework that incorporates surrounding aircraft intent via Dubins-path-based predictions and optimizes a full control sequence over a horizon, with a soft safety constraint to handle uncertainties. Compared to ACAS Xu, the MPC approach improves safety metrics, reduces LDWC incidence, eliminates NMAC events, and yields smoother trajectories even under deterministic and stochastic delays and sensor errors. The results underscore the benefits of proactive, horizon-based planning that leverages intent data, with implications for safer and more efficient operations in automated air mobility; future work will extend to larger multi-agent settings and real-time computational efficiency.

Abstract

In this paper, we propose an intent-aware Model Predictive Control (MPC) approach for the remain-well-clear (RWC) functionality of a multi-agent aircraft detect-and-avoid (DAA) system and compare its performance with the standardized Airborne Collision Avoidance System Xu (ACAS Xu). The aircraft system is modeled as a linear system for horizontal maneuvering, with advisories on the rate of turn as the control input. Both deterministic and stochastic time delays are considered to account for the lag between control guidance issuance and the response of the aircraft. The capability of the MPC scheme in producing an optimal control profile over the entire horizon is used to mitigate the impact of the delay. We compare the proposed MPC method with ACAS Xu using various evaluation metrics, including loss of DAA well-clear percentage, near mid-air collision percentage, horizontal miss distance, and additional flight distance across different encounter scenarios. It is shown that the MPC scheme achieves better evaluation metrics than ACAS Xu for both deterministic and stochastic scenarios.

Figures (14)

• Figure 1: System diagram of the proposed intent-aware MPC framework for multi-aircraft system with response delay for a given aircraft. Intent information is provided as the target state of surrounding aircraft. A Dubins path is used to generate the connecting trajectory. Then the MPC scheme computes the optimal control input sequence, which is used to construct a delay-based policy. This procedure is repeated at each time step.
• Figure 2: Geometry of two aircraft in the horizontal plane in the earth-fixed coordinate system. The black variables are the state variables and the greens are the angular velocities as the control inputs. The linear velocities, shown in blue, are constant.
• Figure 3: An LSR Dubins path for aircraft $j$
• Figure 4: Comparison of the proposed MPC scheme policy for delayed systems with common approaches. The blue dotted-line indicates the current time $t$, while the red dotted line represents the shifted time at $t-\delta$, with the corresponding predicted optimal control sequence shown in red. The shifted sequence is shown in blue dashed-line. Green lines represent policies executed using common approaches (simple shifting) or by the proposed MPC policy (aligning with the corresponding predicted element).
• Figure 5: Comparison of the MPC policies $\boldsymbol{\mathrm{\pi}}_{\mathrm{common}}$ (dashed line) and $\boldsymbol{\mathrm{\pi}}_{\mathrm{delay}}$ (solid line) without sensor errors and for both aircraft equipped, with "slow" response delay and same speeds for relative headings $45$deg (top-left), $90$deg (top-right), $135$deg (bottom-left), $180$deg (bottom-right).