The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Enroute flight planning: The design of cooperative planning systems

Design concepts and principles to guide in the building of cooperative problem solving systems are being developed and evaluated. In particular, the design of cooperative systems for enroute flight planning is being studied. The investigation involves a three stage process, modeling human performance in existing environments, building cognitive artifacts, and studying the performance of people working in collaboration with these artifacts. The most significant design concepts and principles identified thus far are the principle focus

Technical approaches for measurement of human errors

Human error is a significant contributing factor in a very high proportion of civil transport, general aviation, and rotorcraft accidents. The technical details of a variety of proven approaches for the measurement of human errors in the context of the national airspace system are presented. Unobtrusive measurements suitable for cockpit operations and procedures in part of full mission simulation are emphasized. Procedure, system performance, and human operator centered measurements are discussed as they apply to the manual control, communication, supervisory, and monitoring tasks which are relevant to aviation operations

Heuristics to Improve Human Factors Performance in Aviation

This paper reviews literature related to heuristic cognitive strategies as they are used by flight crews. A review of heuristic and naturalistic cognition is presented. An example set of heuristics and cognitive biases are presented and where possible exemplified by 19 airline accidents. The paper suggests two tentative research designs which could be pursued to quantitatively study heuristics and its impact on aviation decision making. The paper concludes that aviation pilot training would benefit by introducing pilots to the concepts and constructs of heuristic thinking

Human Error and Accident Causation Theories, Frameworks and Analytical Techniques: An Annotated Bibliography

Over the last several decades, humans have played a progressively more important causal role in aviation accidents as aircraft have become more [complex]. Consequently, a growing number of aviation organizations are tasking their safety personnel with developing safety programs to address the highly complex and often nebulous issue of human error. However, there is generally no “off-the-shelf” or standard approach for addressing human error in aviation. Indeed, recent years have seen a proliferation of human error frameworks and accident investigation schemes to the point where there now appears to be as many human error models as there are people interested in the topic. The purpose of the present document is to summarize research and technical articles that either directly present a specific human error or accident analysis system, or use error frameworks in analyzing human performance data within a specific context or task. The hope is that this review of the literature will provide practitioners with a starting point for identifying error analysis and accident investigation schemes that will best suit their individual or organizational needs

Modelling the dispersion of aircraft trajectories using Gaussian processes

This work investigates the application of Gaussian processes to capturing the probability distribution of a set of aircraft trajectories from historical measurement data. To achieve this, all data are assumed to be generated from a probabilistic model that takes the shape of a Gaussian process.The approach to Gaussian process modelling used here is based on a linear expansion of trajectory data into set of basis functions that may be parametrized by a multivariate Gaussian distribution. The parameters are learned through maximum likelihood estimation.The resulting probabilistic model can be used for both modelling the dispersion of trajectories along the common flightpath and for generating new samples that are similar to the historical data.The performance of this approach is evaluated using three trajectory datasets; toy trajectories generated from a Gaussian distribution, sounding rocket trajectories that are generated by a stochastic rocket flight simulator and aircraft trajectories on a given departure path from DFW airport, as measured by ground-based radar. The results indicate that the maximum deviation between the probabilistic model and test data obtained for the three data sets are respectively 4.9%, 7.6% and 13.1%

A graph theoretic approach to scene matching

The ability to match two scenes is a fundamental requirement in a variety of computer vision tasks. A graph theoretic approach to inexact scene matching is presented which is useful in dealing with problems due to imperfect image segmentation. A scene is described by a set of graphs, with nodes representing objects and arcs representing relationships between objects. Each node has a set of values representing the relations between pairs of objects, such as angle, adjacency, or distance. With this method of scene representation, the task in scene matching is to match two sets of graphs. Because of segmentation errors, variations in camera angle, illumination, and other conditions, an exact match between the sets of observed and stored graphs is usually not possible. In the developed approach, the problem is represented as an association graph, in which each node represents a possible mapping of an observed region to a stored object, and each arc represents the compatibility of two mappings. Nodes and arcs have weights indicating the merit or a region-object mapping and the degree of compatibility between two mappings. A match between the two graphs corresponds to a clique, or fully connected subgraph, in the association graph. The task is to find the clique that represents the best match. Fuzzy relaxation is used to update the node weights using the contextual information contained in the arcs and neighboring nodes. This simplifies the evaluation of cliques. A method of handling oversegmentation and undersegmentation problems is also presented. The approach is tested with a set of realistic images which exhibit many types of sementation errors

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 336)

This bibliography lists 111 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance