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  1. The operational safety of Automated Driving System (ADS)-Operated Vehicles (AVs) are a rising concern with the deployment of AVs as prototypes being tested and also in commercial deployment. The robustness of safety evaluation systems is essential in determining the operational safety of AVs as they interact with human-driven vehicles. Extending upon earlier works of the Institute of Automated Mobility (IAM) that have explored the Operational Safety Assessment (OSA) metrics and infrastructure-based safety monitoring systems, in this work, we compare the performance of an infrastructure-based Light Detection And Ranging (LIDAR) system to an onboard vehicle-based LIDAR system in testing at the Maricopa County Department of Transportation SMARTDrive testbed in Anthem, Arizona. The sensor modalities are located in infrastructure and onboard the test vehicles, including LIDAR, cameras, a real-time differential GPS, and a drone with a camera. Bespoke localization and tracking algorithms are created for the LIDAR and cameras. In total, there are 26 different scenarios of the test vehicles navigating the testbed intersection; for this work, we are only considering car following scenarios. The LIDAR data collected from the infrastructure-based and onboard vehicle-based sensors system are used to perform object detection and multi-target tracking to estimate the velocity and position information of the test vehicles and use these values to compute OSA metrics. The comparison of the performance of the two systems involves the localization and tracking errors in calculating the position and the velocity of the subject vehicle, with the real-time differential GPS data serving as ground truth for velocity comparison and tracking results from the drone for OSA metrics comparison.

     
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  2. Abstract. In this study, we developed a novel algorithm based on the collocatedModerate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR)observations and dust vertical profiles from the Cloud–Aerosol Lidar withOrthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosoloptical depth at 10 µm (DAOD10 µm) and the coarse-mode dusteffective diameter (Deff) over global oceans. The accuracy of theDeff retrieval is assessed by comparing the dust lognormal volumeparticle size distribution (PSD) corresponding to retrieved Deff withthe in situ-measured dust PSDs from the AERosol Properties – Dust(AER-D), Saharan Mineral Dust Experiment (SAMUM-2), and Saharan Aerosol Long-Range Transport and Aerosol–Cloud-InteractionExperiment (SALTRACE) fieldcampaigns through case studies. The new DAOD10 µm retrievals wereevaluated first through comparisons with the collocated DAOD10.6 µmretrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOPobservations from our previous study (Zheng et al., 2022). The pixel-to-pixelcomparison of the two DAOD retrievals indicates a good agreement(R∼0.7) and a significant reduction in (∼50 %) retrieval uncertainties largely thanks to the better constraint ondust size. In a climatological comparison, the seasonal and regional(2∘×5∘) mean DAOD10 µm retrievals basedon our combined MODIS and CALIOP method are in good agreement with the twoindependent Infrared Atmospheric Sounding Interferometer (IASI) productsover three dust transport regions (i.e., North Atlantic (NA; R=0.9),Indian Ocean (IO; R=0.8) and North Pacific (NP; R=0.7)). Using the new retrievals from 2013 to 2017, we performed a climatologicalanalysis of coarse-mode dust Deff over global oceans. We found thatdust Deff over IO and NP is up to 20 % smaller than that over NA.Over NA in summer, we found a ∼50 % reduction in the numberof retrievals with Deff>5 µm from 15 to35∘ W and a stable trend of Deff average at 4.4 µm from35∘ W throughout the Caribbean Sea (90∘ W). Over NP inspring, only ∼5 % of retrieved pixels with Deff>5 µm are found from 150 to 180∘ E, whilethe mean Deff remains stable at 4.0 µm throughout eastern NP. To the best of our knowledge, this study is the first to retrieve both DAOD andcoarse-mode dust particle size over global oceans for multiple years. Thisretrieval dataset provides insightful information for evaluating dustlongwave radiative effects and coarse-mode dust particle size in models.

     
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