Nuisance Alerts Research Articles

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

Creating Meaningful Alerts and Reducing Alert Fatigue: Strategies Implemented by Informatics Pharmacists to Optimize Dose Range Checking Alerts in a Multihospital Health System.

Background: Among the many clinical decision support (CDS) mechanisms available in electronic health record (EHR) systems, dose range checking (DRC) is one of the most impactful safeguard tools integrated within most computerized provider order entry (CPOE) workflows. Unfortunately, improper configurations and lack of resources to maintain and monitor CDS systems can hinder and even disrupt daily clinical operations. Objective: This article seeks to highlight the impact that informatics pharmacists can make by implementing different strategies to decrease nuisance alerts and create clinically meaningful DRC alerts that guide clinicians in their practice. Methods: Following the activation of the DRC application for 3623 medication groupers (ie, generic drugs and all their dosage form variations), informatics pharmacists implemented strategies to monitor DRC alert output and decrease the number of inappropriate alerts. Such strategies included weekly monitoring of alerts, modification of order sentences (including dose, route, and age/weight filters), update to the rule triggering the alerts, and modifications of the preference settings. Results: From July to September 2018, an average of 70 DRC tables were reviewed by informatics pharmacists, reducing the number of overridden DRC alerts to 4796 in the first week of September-a 63% decrease in a 3-month period. Conclusions: By reducing the number of DRC nuisance alerts and improving the clinical content of DRC alerts, informatics pharmacists can contribute to lowering alert fatigue and improving providers' trust in CDS alerts.

Statistical models for the lidar technology: false alarms, receiver operating characteristic curves, and Swerling models

We argue for the integration of the statistical models already widely used in radar technology into lidar technology. The aim is to assess the validity or degree of confidence of an alert to be issued in view of not overloading the pilot with nuisance alerts. We present the basics of the detection theory. We give three examples of simulations illustrating the use of these statistical models either for designing lidars or for preparing lidar missions. We describe the simulator having been developed and used. We also present the idea of developing mixtures of statistical models as an approach to thresholding and object classification at mission time. Some experimental data are presented to validate both the simulator output and the use of mixtures of models for object segmentation or classification.

Quantifying and recommending seat belt reminder timing using naturalistic driving video data

Introduction: To better understand the timing of when people buckle their seat belt, an analysis of a naturalistic driving study was used. The study provided a unique perspective inside of the vehicle where the entire seat belt was visible from the time the driver entered the vehicle to one minute of driving forward or 32 kph. Method: Seat belt buckling behavior was identified for 30 drivers. An additional 10 drives for 13 of these drivers were identified for a seat belt sequencing, which identified the points when the vehicle was put into ignition, shifted, when vehicle movement began, and when the seat belt was buckled. The speed at belt closure was also identified. The timing from ignition to buckle and to shifting into forward gear were examined to identify the speed and appropriate timing for seat belt reminders. Results: The data show that drivers were buckled in over 92% of the 3,102 drives. In addition, in 70% of those total drives, the drivers were buckled before the vehicle began movement. Of greater interest for seat belt reminders/interlocks are those drives when drivers buckle after movement. When considering time from ignition to seat belt closure, the mean was 27.5 s. Because higher speeds are typically reached when traveling forward rather than reverse, it was important to know the time duration from shifting into drive to buckling. With this consideration, the mean to buckle dropped to 16.2 s. The mean speed at buckling when traveling forward was 15.3 kph. From the regression analysis, the input variables ‘Age,’ ‘Sex,’ ‘Weight,’ ‘Environment,’ and ‘Weather’ are significant contributors in predicting the log odds of a driver putting on seatbelt. Conclusions: With the understanding that higher speeds lead to an increased risk of injury and/or death and with the results of the analysis, a recommendation of a 30 s time from forward shift and a 25 kph (6.9 m/s) threshold for reminder systems should be implemented. The regression analysis also validates that most of the predicted seat belt buckling times are within 30 s. Practical Applications: This would reduce perception of nuisance alerts and protect the driver from higher speed unbuckled crashes. The seat belt buckling time prediction model also demonstrates good potential for developing tailored buckling warning system for different drivers.

TCAS algorithm for general aviation based on ADS-B

This paper proposes a new traffic alert and collision avoidance system (TCAS) for general aviation (GA) that uses automatic dependent surveillance-broadcast (ADS-B). The proposed GA-TCAS alerts the pilot of upcoming potential conflicts, which are becoming increasingly common as the number of GA movements increases. Conflict detection and avoidance uses the ADS-B and GA maneuvers. The algorithm selects the angle according to the intruder’s heading and position in order to reduce the nuisance alerts. The detection algorithm has three stages: proximate advisory, traffic advisory, and resolution advisory. The relationship between the turn angle and the horizontal miss distance (HMD) are calculated to determine a resolution when an intruder enters the separation bubble. Using the encounter models for visual flight rule (VFR) to evaluate the performance of this TCAS algorithm, the resolution should follow the VFR and prioritize in different situations. In flight simulations, the proposed GA-TCAS is tested using real ultralight flight data. The system performance is demonstrated using flight trajectories from conflict detection and resolution.

Nuisance Alerts in Operational ATC Environments: Classification and Frequencies

Nuisance alerts can cause many problems in operational settings. They are distracting and can lead to desensitization. In Air Traffic Control (ATC), there has been much anecdotal evidence regarding the high rate of nuisance alerts in facilities, but there have been few formal studies to evaluate this problem. In this study, we measured the rate of nuisance alerts in en route and terminal ATC facilities. After calculating the average duration of alerts, the percentage of alerts that receive a controller response, and the timing of responses to these alerts, we estimate that 62% of Conflict Alerts (CAs) and 91% of Minimum Safe Altitude Warnings (MSAWs) in en route, and 44% of CAs and 61% of MSAWs in terminal are unnecessary. Using human factors principles, we make recommendations for improving the accuracy and utility of ATC alerts.

Methodology for alerting-system performance evaluation

A probabilistic-analysis methodology is described that provides quantitative measures of alerting-system performance, including the probabilities of a false alarm and missed detection. As part of the approach, the alerting decision is recast as a signal-detection problem, and system operating-characteristic curves are introduced to describe the tradeoffs between alerting-threshold placement and system performance. The methodology fills the need for a means to determine appropriate alerting thresholds and to quantify the potential benefits that are possible through changes in the design of the system. Because the methodology is developed in a generalized manner, it can be used in a variety of vehicle, transportation-system, and process-control applications. The methodology is demonstrated through an example application to the Traffic Alert and Collision Avoidance System (TCAS). Recent changes in TCAS alerting thresholds are shown to reduce the probability of a false alarm in situations known to produce frequent nuisance alerts in actual operations. Nomenclature A, N, T = probabilistic-state trajectories E = event of encountering a hazard fx (x) = probability density function for the random variable x h = estimated relative altitude h = estimated relative-altitude rate / = incident event ^maif - probability of alerting-system malfunction P(x) = probability of event ;c PT(X) = probability of event x evaluated along trajectory T r = estimated relative range r = estimated relative-range rate