Decentralized Conflict Detection and Resolution Using Intent-Based Probabilistic Trajectory Prediction

Yokoyama, Nobuhiro

doi:10.2514/6.2018-1857

Cited by 11 publications

(8 citation statements)

References 20 publications

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“…Model predictive control (MPC) is also a promising approach to conflict resolution. Yokohama [9] applied MPC to perform trajectory prediction and conflict resolution simultaneously, in which the aircraft separation condition is implicitly imposed during trajectory prediction. However, the mathematical model is highly complex, and the resolution quality depends on the quality (noise-free) of available historical data.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties

Delahaye

Pham

Tran

et al. 2022

Transportation Research Part C: Emerging Technologies

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With the continuous growth in the air transportation demand, air traffic controllers will have to handle increased traffic and consequently, more potential conflicts. This gives rise to the need for conflict resolution advisory tools that can perform well in high-density traffic scenarios given a noisy environment.Unlike model-based approaches, learning-based approaches can take advantage of historical traffic data and flexibly encapsulate environmental uncertainty. In this study, we propose a reinforcement learning approach that is capable of resolving conflicts, in the presence of traffic and inherent uncertainties in conflict resolution maneuvers, without the need for prior knowledge about a set of rules mapping from conflict scenarios to expected actions. The conflict resolution task is formulated as a decision-making problem in a large and complex action space.The research also includes the development of a learning environment, scenario state representation, reward function, and a reinforcement learning algorithm inspired from Q-learning and Deep Deterministic Policy Gradient algorithms.The proposed algorithm, with two stages decision-making process, is used to train an agent that can serves as an advisory tool for air traffic controllers in resolving air traffic conflicts where it can learn from historical data by evolving overtime. Our findings show that the proposed model gives the agent the

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Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties

Delahaye

Pham

Tran

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…A comprehensive summary of early mathematical methods for automated conflict resolution could be found in [2]. More recently, different approaches have been proposed to improve the performance of automated conflict resolver [3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Interactive Conflict Solver for Learning Air Traffic Conflict Resolutions

Tran

Pham

Goh

et al. 2020

Journal of Aerospace Information Systems

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The increasing demand in air transportation is pushing the current air traffic management (ATM) system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic, specifically in resolving potential conflict. Since current automated conflict resolutions are not in conformance with the thinking or preferences of individual ATCOs, consequently, they are unlikely accepted by the ATCOs. In this work, we build an artificial intelligence system as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs' demonstrations, and an AI agent. The AI Agent is based on reinforcement learning to suggest conflict resolutions. We observe that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent's performance but also gives it the capability to suggest more human-like resolutions.That could help to increase the ATCOs' acceptance rate of the agent's suggested resolutions.Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.

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“…An example of this type of method can be found in [12], in which state propagation requires knowledge of agent dynamics. The author of [12] estimates the obstacle's intention using knowledge of its optimization method and cost function. With the assumption that the obstacle is attempting to minimize a certain cost function, the inverse of the optimization procedure is computed to predict the optimal trajectory.…”

Section: Introductionmentioning

confidence: 99%

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification

Patterson

Lakshmanan

Hovakimyan

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles' motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle's future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velocity measurements to predict collisions with dynamic obstacles. We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression. To control the growth of uncertainty over arbitrary time horizons, a probabilistic obstacle intention is assumed as a distribution over obstacle positions and velocities, which can be naturally included in the Gaussian process framework. Our approach is demonstrated in two case studies in which (i), an obstacle overtakes the agent and (ii), an obstacle crosses the agent's path perpendicularly. In these simulations we show that the collision can be predicted despite having limited knowledge of the obstacle's behavior.

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Decentralized Conflict Detection and Resolution Using Intent-Based Probabilistic Trajectory Prediction

Cited by 11 publications

References 20 publications

Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties

Deep reinforcement learning based path stretch vector resolution in dense traffic with uncertainties

An Interactive Conflict Solver for Learning Air Traffic Conflict Resolutions

Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification

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