Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.

Relational Database for Horizontal-to-Vertical Spectral Ratios

Frequency-dependent horizontal-to-vertical spectral ratios (HVSRs) of Fourier amplitudes from three-component recordings can provide useful information for site response modeling. However, such information is not incorporated into most ground-motion models, including those from Next-Generation Attenuation projects, which instead use the time-averaged shear-wave velocity (VS) in the upper 30 m of the site and sediment depth terms. To facilitate utilization of HVSR, we developed a publicly accessible relational database. This database is adapted from a similar repository for VS data and provides microtremor-based HVSR data (mHVSR) and supporting metadata, but not parameters derived from the data. Users can interact with the data directly within a web portal that contains a graphical user interface (GUI) or through external tools that perform cloud-based computations. Within the database GUI, the median horizontal-component mHVSR can be plotted against frequency, with the mean and mean ± one standard deviation (representing variability across time windows) provided. Using external interactive tools (provided as a Jupyter Notebook and an R script), users can replot mHVSR (as in the database) or create polar plots. These tools can also derive parameters of potential interest for modeling purposes, including a binary variable indicating whether an mHVSR plot contains peaks, as well as the fitted properties of those peaks (frequencies, amplitudes, and widths). Metadata are also accessible, which includes site location, details about the instruments used to make the measurements, and data processing information related to windowing, antitrigger routines, and filtering.

NGA-Subduction research program

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

Ranking and Selection of Earthquake Ground-Motion Models Using the Stochastic Area Metric

We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

A Stochastic Model for Simulating Vertical Pulseless Near-Fault Seismic Ground Motions

ABSTRACT The vertical near-fault seismic ground-motion component can cause significant structural deformation and damage, which can be evaluated from time history analysis using actual or synthetic ground-motion records. In this study, we propose a new stochastic model for the vertical pulseless near-fault ground motions that depends on earthquake magnitude, rupture distance, and site condition. The proposed model is developed based on the time–frequency characteristics of 606 selected actual vertical record components in strike-slip earthquakes. The use and validation of the model are presented using simulated records obtained by two simulation techniques. For the validation, the statistics of time–frequency-dependent power spectral acceleration estimated from the simulated records using the proposed stochastic model are compared with those from the actual records and the ground-motion models available in the literature.