James K. Kuchar

Bio: James K. Kuchar is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Air traffic control & Cockpit. The author has an hindex of 25, co-authored 81 publications receiving 3261 citations.

A review of conflict detection and resolution modeling methods

Abstract: A number of methods have been proposed to automate air traffic conflict detection and resolution (CDR), but there has been little cohesive discussion or comparative evaluation of approaches. The paper presents a survey of 68 CDR modeling methods, several of which are currently in use or under operational evaluation. A framework that articulates the basic functions of CDR is used to categorize the models. The taxonomy includes: dimensions of state information (vertical, horizontal, or three-dimensional, 3-D); method of dynamic state propagation (nominal, worst case, or probabilistic); conflict detection threshold; conflict resolution method (prescribed, optimized, force field, or manual); maneuvering dimensions (speed change, lateral, vertical, or combined manoeuvres); and management of multiple aircraft conflicts (pairwise or global). An overview of important considerations for these and other CDR functions is provided, and the current system design process is critiqued.

The Traffic Alert and Collision Avoidance System

Abstract: VOLUME 16, NUMBER 2, 2007 LINCOLN LABORATORY JOURNAL 277 tinuously on a jet transport aircraft in today’s environment could expect to survive more than 11,000 years of travel before becoming the victim of a mid-air collision. This accomplishment has only recently been realized. As shown in Figure 1, the number of hours flown annually by jet transport aircraft has more than quadrupled since 1970, but the rate of mid-air collisions over that period of time has dropped by an order of magnitude. The result is that today we can expect one mid-air collision every 100 million flight hours. Such an exceptional safety level was achieved through advances in air traffic surveillance technology and relentless attention to improving operational procedures. But as the September 2006 mid-air collision between a Boeing 737 and an Embraer Legacy 600 business jet over the Amazon jungle in Brazil demonstrates, maintaining safety is an ever present challenge. This challenge has been eased, but not eliminated, with the development and deployment of TCAS. TCAS is one component of a multi-layered defense against mid-air collisions. The structure of airspace and A collision between aircraft is one of the most sudden and catastrophic transportation accidents imaginable. These tragic events are rarely survivable—hundreds of people may die as the two aircraft are destroyed. In response to this threat, Lincoln Laboratory has been pursuing surveillance and alerting system technologies to protect aircraft operations both on the ground and in the air. Recent developments in the Runway Status Lights Program, for example, greatly reduce airport-surface collision risk due to runway incursions [1]. In the air, other systems have been developed and are currently in use to prevent midair collisions. This article focuses on the widely fielded, crucial technology called the Traffic Alert and Collision Avoidance System (TCAS). In the context of integrated sensing and decision support, TCAS illustrates the particular challenge of developing effective decision aids for use in emergency situations involving extreme time pressure. Despite the terrifying prospect of a mid-air collision, aviation travel is incredibly safe. A person who flew conThe Traffic Alert and Collision Avoidance System

Survey of conflict detection and resolution modeling methods

Abstract: The design and evaluation of traffic conflict detection and resolution systems requires the use of analytical models that describe encounter dynamics and the costs and benefits of avoidance actions. A number of such models have been applied in the past to the problem, but there has been no cohesive discussion or comparative evaluation of these approaches. Each method has benefits and limitations, and future efforts may be facilitated by combining the best features of different techniques. This paper presents a summary of conflict detection and resolution modeling approaches. Modeling techniques are categorized and the fundamental assumptions, capabilities, and limitations of each approach are described. The methods are evaluated and compared based on their applicability to free flight traffic conflict situations.

Airspace Encounter Models for Estimating Collision Risk

Abstract: Airspace encounter models, providing a statistical representation of geometries and aircraft behavior during a close encounter, are required to estimate the safety and robustness of collision avoidance systems. Prior encounter models, developed to certify the Traffic Alert and Collision Avoidance System, have been limited in their ability to capture important characteristics of encounters as revealed by recorded surveillance data, do not capture the current mix of aircraft types or noncooperative aircraft, and do not represent more recent airspace procedures. This paper describes a methodology for encounter model construction based on a Bayesian statistical framework connected to an extensive set of national radar data. In addition, this paper provides examples of using several such high-fidelity models to evaluate the safety of collision avoidance systems for manned and unmanned aircraft.

Collision Avoidance for Unmanned Aircraft using Markov Decision Processes

Abstract: we investigate the automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder behavior. By formulating the problem of collision avoidance as a Markov Decision Process (MDP) for sensors that provide precise localization of the intruder aircraft, or a Partially Observable Markov Decision Process (POMDP) for sensors that have positional uncertainty or limited eld-of-view constraints, generic MDP/POMDP solvers can be used to generate avoidance strategies that optimize a cost function that balances ight-plan deviation with collision. Experimental results demonstrate the suitability of such an approach using four dierent sensor modalities and a parametric aircraft performance model.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Toward a Theory of Situation Awareness in Dynamic Systems

Abstract: This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directio...

Unscented filtering and nonlinear estimation

Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.

A model for types and levels of human interaction with automation

Abstract: We outline a model for types and levels of automation that provides a framework and an objective basis for deciding which system functions should be automated and to what extent. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.

Humans and automation: Use, misuse, disuse, abuse

Abstract: This paper addresses theoretical, empirical, and analytical studies pertaining to human use, misuse, disuse, and abuse of automation technology. Use refers to the voluntary activation or disengagement of automation by human operators. Trust, mental workload, and risk can influence automation use, but interactions between factors and large individual differences make prediction of automation use difficult. Misuse refers to over reliance on automation, which can result in failures of monitoring or decision biases. Factors affecting the monitoring of automation include workload, automation reliability and consistency, and the saliency of automation state indicators. Disuse, or the neglect or underutilization of automation, is commonly caused by alarms that activate falsely. This often occurs because the base rate of the condition to be detected is not considered in setting the trade-off between false alarms and omissions. Automation abuse, or the automation of functions by designers and implementation by man...