AI Enabled Maneuver Identification via the Maneuver Identification Challenge

  title={AI Enabled Maneuver Identification via the Maneuver Identification Challenge},
  author={Kaira Samuel and Matthew LaRosa and Kyle McAlpin and Morgan Schaefer and Brandon Swenson and Devin Wasilefsky and Yan Wu and Dan Zhao and Jeremy Kepner},
Flight simulators play a critical role in pilot training. Current training paradigms require scarce, highly-experienced instructor pilots to teach even the most basic flight maneuvers, beginning with basic flight maneuver familiarization in flight simulators. AI has significant potential to enhance simulator-based training by providing real-time feedback on the quality of each flight maneuver to student pilots for early-stage learning. An important first step towards achieving AI enhanced pilot… 

Figures and Tables from this paper



Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

A hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles to demonstrate a high performance compared to various existing methods.

An adaptive maneuvering logic computer program for the simulation of one-to-one air-to-air combat. Volume 2: Program description

A detailed description is presented of the computer programs in order to provide an understanding of the mathematical and geometrical relationships as implemented in the programs. The individual

Unsafe Maneuver Classification From Dashcam Video and GPS/IMU Sensors Using Spatio-Temporal Attention Selector

A novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data that leverages multi-head dot product attention to select the relevant ones, i.e., the dangerous ones or the ones in danger, to perform classification is proposed.

Joint Deep Neural Network Modelling and Statistical Analysis on Characterizing Driving Behaviors

This study focuses on detecting the sematic-level driving behaviors from large-scale GPS sensor data and proposes a joint histogram feature map to regularize the shallow features in this paper.

A reflection on seven years of the VAST challenge

The VAST Challenge has provided an opportunity for visual analytics researchers to test their innovative thoughts on approaching problems in a wide range of subject domains against realistic datasets and problem scenarios.

Sparse Deep Neural Network Graph Challenge

The proposed Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems.

Static graph challenge: Subgraph isomorphism

The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems.

Streaming graph challenge: Stochastic block partition

This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity that employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs.

Maneuver dtection and scoring in the pilot training next v1.0 datasett," tech. rep., SAF/CO Chief Data Office

  • 2019