The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.Comment: In Proceedings SCAV 2018, arXiv:1804.0340

The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.National Science Foundation; Air Force Office of Scientific Research [1253334, 1262960, 1419419, 1446435, 1446690, 1446702, 1446715 1521617]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Music theory on marimba: bringing the classroom into the practice room

Since its emergence in the twentieth century, the teaching of four-mallet marimba playing has become standard in the college percussion curriculum. An enormous increase in the volume and difficulty of literature for the instrument in the last two decades has led to an equal increase in the level of musical understanding necessary to perform on the marimba. In the first years of college instruction, the development of four-mallet skills is vital to a student's sound production, technical accuracy, and overall musicianship. Equally as important, academic success in courses such as music theory during these same years is crucial to the development of analytical and interpretive skills. The purpose of this study is to create a book of etudes that brings together four-mallet marimba technique and music theory. The connection between what a student is learning in music theory class and what he or she works on in the practice room is incredibly important to the development of a young musician. A better analytical understanding of a piece can lead to a more informed, complete, and accurate performance. Similarly, technical fluency helps alleviate difficulties and serve musicality in performance. The creation of parallel sequences of topics for both areas resulted in a logical theoretical and technical progression through ten etudes. The first years of collegiate music study may be a student's initial experience with four-mallet marimba, music theory, or both. While proper instruction in both areas is obviously essential to success, the connection between the two areas is even more important. Although referenced often in private instruction, such a combination of music theory and four-mallet technique does not currently exist, despite its value to the student and educator. As a result, there is often a gap between the classroom and the practice room. This document is meant to help close that gap and allow students to recognize the relevance of their coursework to their time in the practice room

Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA

Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm−2 year−1) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis (sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression (sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGLdw) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGLdw. However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass

Derived electron densities from linear polarization observations of the visible-light corona during the 14 December 2020 total solar eclipse

A new instrument was designed to take visible-light (VL) polarized brightness ($pB$) observations of the solar corona during the 14 December 2020 total solar eclipse. The instrument, called the Coronal Imaging Polarizer (CIP), consisted of a 16 MP CMOS detector, a linear polarizer housed within a piezoelectric rotation mount, and an f-5.6, 200 mm DSLR lens. Observations were successfully obtained, despite poor weather conditions, for five different exposure times (0.001 s, 0.01 s, 0.1 s, 1 s, and 3 s) at six different orientation angles of the linear polarizer (0\de, 30\de, 60\de, 90\de, 120\de, and 150\de). The images were manually aligned using the drift of background stars in the sky and images of different exposure times were combined using a simple signal-to-noise ratio cut. The polarization and brightness of the local sky is also estimated and the observations were subsequently corrected. The $pB$ of the K-corona was determined using least squares fitting and radiometric calibration was done relative to the Mauna Loa Solar Observatory (MLSO) K-Cor $pB$ observations from the day of the eclipse. The $pB$ data was then inverted to acquire the coronal electron density, $n_e$, for an equatorial streamer and a polar coronal hole, which agreed very well with previous studies. The effect of changing the number of polarizer angles used to compute the $pB$ is also discussed and it is found that the results vary by up to $\sim$ 13\% when using all six polarizer angles versus only a select three angles

Enabling Mixed Autonomy Traffic Control

We demonstrate a new capability of automated vehicles: mixed autonomy traffic control. With this new capability, automated vehicles can shape the traffic flows composed of other non-automated vehicles, which has the promise to improve safety, efficiency, and energy outcomes in transportation systems at a societal scale. Investigating mixed autonomy mobile traffic control must be done in situ given that the complex dynamics of other drivers and their response to a team of automated vehicles cannot be effectively modeled. This capability has been blocked because there is no existing scalable and affordable platform for experimental control. This paper introduces an extensible open-source hardware and software platform, enabling a team of 100 vehicles to execute several different vehicular control algorithms as a collaborative fleet, composed of three different makes and models, which drove 22752 miles in a combined 1022 hours, over 5 days in Nashville, TN in November 2022

Traffic smoothing using explicit local controllers

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.Comment: 21 pages, 1 Table , 9 figure

Land management explains major trends in forest structure and composition over the last millennium in California's Klamath Mountains

For millennia, forest ecosystems in California have been shaped by fire from both natural processes and Indigenous land management, but the notion of climatic variation as a primary controller of the pre-colonial landscape remains pervasive. Understanding the relative influence of climate and Indigenous burning on the fire regime is key because contemporary forest policy and management are informed by historical baselines. This need is particularly acute in California, where 20th-century fire suppression, coupled with a warming climate, has caused forest densification and increasingly large wildfires that threaten forest ecosystem integrity and management of the forests as part of climate mitigation efforts. We examine climatic versus anthropogenic influence on forest conditions over 3 millennia in the western Klamath Mountains—the ancestral territories of the Karuk and Yurok Tribes—by combining paleoenvironmental data with Western and Indigenous knowledge. A fire regime consisting of tribal burning practices and lightning were associated with long-term stability of forest biomass. Before Euro-American colonization, the long-term median forest biomass was between 104 and 128 Mg/ha, compared to values over 250 Mg/ha today. Indigenous depopulation after AD 1800, coupled with 20th-century fire suppression, likely allowed biomass to increase, culminating in the current landscape: a closed Douglas fir–dominant forest unlike any seen in the preceding 3,000 y. These findings are consistent with precontact forest conditions being influenced by Indigenous land management and suggest large-scale interventions could be needed to return to historic forest biomass levels

Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p<0.05) were generated for all 23 forest metrics using both the DR and FW lidar datasets, with R2 values for the best fit models in the range R2=0.43-0.94 for the DR data and R2=0.28-0.97 for the FW data (with normalised RMSE values being 18%-66% and 16%-48% respectively). For all but two forest metrics the difference between the NRMSE of the best performing DR and FW models was ≤7%, and there was an even split (11:12) as to which lidar dataset (DR or FW) generated the best model per forest metric. Overall, the DR data performed better at modelling structure variables, whilst the FW data performed better at modelling composition and deadwood variables. Neither showed a clear advantage at modelling variables from a particular vegetation layer (canopy, shrub or ground). Height, intensity/amplitude, and ITC-derived crown variables were shown to be important inputs across the best performing models (DR or FW), but the additional echo-width variables available from FW point data were relatively unimportant. Of perhaps greater significance to the choice between lidar data type (i.e. DR or FW) in determining the predictive power of the best performing models was the selection of leaf-on and/or leaf-off data. Of the 23 best models, 10 contained both leaf-on and leaf-off lidar variables, whilst 11 contained only leaf-on and two only leaf-off data. We therefore conclude that although FW lidar has greater vertical profile information than DR lidar, the greater complimentary information about the entire forest canopy profile that is available from both leaf-on and leaf-off data is of more benefit to forest inventory, in general, than the selection between DR or FW lidar

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference