A Generative Adversarial Imitation Learning Approach for Realistic Aircraft Taxi-Speed Modeling

Abstract

Classical approaches for modelling aircraft taxispeed assume constant speed or use a turning rate function to approximate taxi-timings for taxiing aircraft. However, those approaches cannot predict spatio-temporal component of aircrafttaxi trajectory due to a lack of consideration of the complexity and stochasticity of airport-airside movements and interactions. This research adopts the Generative Adversarial Imitation Learning (GAIL) algorithm for aircraft taxi-speed modelling, while considering multiple operational factors including surrounding traffic on the ground and target take-off time. The proposed model can learn and reproduce the ground movement patterns in a real-world dataset under different circumstances. In addition, the characteristics of the taxi-speed model are also analyzed, especially focusing on handling conflict scenarios with surrounding traffic. Finally, the travel-time of the aircraft from starting to target positions are compared with baseline models and actual taxiing data. The proposed model outperforms all the baseline models with a significant margin. In terms of spatial completion (SC), it achieves up to 97.1% for arrivals and 88.3% for departures. The results also show significantly high performance for temporal completion. The model achieves a stable performance with low Root Mean Square Error (RMSE) (16.8 seconds for arrivals, 32.4 seconds for departures) and Mean Absolute Percentage Error (MAPE) (4.4% for arrivals and 7.6% for departures). Our model's errors are 72% lower for arrivals and 48% lower for departures when compared to other baseline models.

An AUV Target-Tracking Method Combining Imitation Learning and Deep Reinforcement Learning

“…SAC is a representative algorithm of offline RL that can realize low-level control of quadrotors and map-free navigation and obstacle avoidance of hybrid unmanned underwater vehicles [49,50]. GAIL is a representative IL algorithm that predicts airport-airside motion of aircraft-taxi trajectories and enables mobile robots to learn to navigate in dynamic pedestrian environments in a socially desirable manner [51,52].…”

DeepIPC: Deeply Integrated Perception and Control for an Autonomous Vehicle in Real Environments

“…By using the end-to-end imitation learning strategy, we can create a single deep learning model to imitate the behavior of an expert driver in manipulating navigational controls or effectors for handling complicated situations on the street [35] [36]. This can be derived from publicly available datasets or simulated with a simulator to enrich the model's driving experiences [37] [38]. Therefore, the model will be able to perform human-like autonomous driving [39].…”

The Design of Mechanisms via Artificial Intelligence

“…AI-enabled systems can analyze data from aircraft and weather sensors to predict and prevent collisions, optimize flight routes, and improve the overall safety and efficiency of air travel. For example, generative AI models can be used to create realistic simulations of air traffic scenarios, which can be used to test and improve air traffic control systems (Pham and et al, 2021).…”