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Machine Learning-Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications
No full textPrecisely understanding the driving environment and determining the vehicle\u27s accurate position is crucial for a safe automated maneuver. vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLOv8 image segmentation, trained on a diverse world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to world measurements. This design minimizes the sensitivity of offset estimation to lane detection accuracy and vehicle orientation. Compared to the existing deep learning-based depth perception models and stereo vision systems, our calibration method significantly improves postprocessing time and minimizes the impacts of the processing delay on the vehicle following system energy efficiency. To assess the accuracy and processing time, we implemented the model on an instrumented L4-capable vehicle and conducted automated vehicle following tests in a controlled environment. In our tests, the model achieved a high level of accuracy, with a biased error of only 0.214 m and a random walk error standard deviation of 0.135 m, demonstrating its reliability across various environmental conditions and ensuring precise lane tracking. Results demonstrate reliable performance across various environmental conditions and sensor noise levels, ensuring precise lane tracking and enhanced automated maneuvering
Effect of Precipitation on Buffer Strip Design and Water Quality: Theory, Case Studies, and Economics
No full textClimate change is predicted to increase precipitation intensity extremes, both flooding and drought, and will therefore necessitate the augmentation of many water infrastructure projects, including buffer strips for protecting waterways from agricultural chemical runoff. To quantify the effect of increased precipitation intensity on buffer strip length, both theory and modeling were employed here. First, design equations were used with varying values of precipitation intensity and the buffer strip width quantified. Next, four case study examples from varying climate zones in the US were modeled using the EPA\u27s Pollutant Load Estimation Tool (PLET) for various chemical constituents and the sensitivity to buffer strip area calculated. The economics of buffer strip and optimal length were also found. It was found from the design equations that buffer strip length was a logarithmic function with rapidly increasing length at low precipitation intensity values but less influence at higher intensity values. In addition, modeling showed that pollutant load decreased quickly with increased buffer area at low values but less so at higher values. Also, the optimal profit of revenue minus cost came at a percent area of buffer strip to croplands area of from 2% to 10%
Reduction-Driven Mobilization of Structural Fe in Clay Minerals with High Fe Content
Get PDFClay minerals contain significant amounts of Fe in their alumosilicate framework, and this structural Fe can be reduced and re-oxidized, constituting a potentially renewable source of reduction equivalents in sedimentary environments. However, dissolution and/or clay mineral transformations during microbial Fe reduction contradict this concept. Here, we investigate how Fe reduction and re-oxidation affect the propensity of Fe to be released from the clay mineral structure and use selective sequential extractions in combination with Mössbauer spectroscopy. Negligible amounts of Fe were released in the sequential extraction of high Fe content clay minerals NAu-1 and NAu-2. Once aqueous Fe(II) was added as a reductant, the extraction procedure recovered the initially added Fe amount and up to 30% of the Fe from the clay mineral structure as both Fe(II) and Fe(III). Similar extents of Fe mobilization were found for clay minerals partly reduced (7%–20%) with dithionite, suggesting that mobilization was reduction-induced and independent of the source of reduction equivalents (Fe(II), dithionite). Although higher Fe reduction extents mobilized more structural Fe, i.e., \u3e90% in fully reduced clay minerals, re-oxidation largely reverted the reduction-induced Fe mobilization in clay minerals. Our finding of reduction-driven Fe mobilization provides a plausible explanation for conflicting reports on Fe release from clay minerals and how extensive Fe atom exchange between aqueous and clay mineral Fe occurs
Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Fixed-Tilt Systems in the U.S. Eastern Interconnection for Weather Year 2015
No full textUsing 2015 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be 30-degree, fixed tilt systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Fixed-Tilt Systems in the U.S. Eastern Interconnection for Weather Year 2021
No full textUsing 2021 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be 30-degree, fixed tilt systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2015
No full textUsing 2015 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2022
No full textUsing 2022 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory’s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be single-axis tracking systems. Sites’ latitudes and longitudes were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadata
Hourly Simulated Power Production Data with No Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2014
No full textUsing 2014 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory\u27s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadat
Hourly Simulated Power Production Data with No Snow Loss Model at Queued Utility-Scale PV Sites Simulated as Single-Axis Tracking Systems in the U.S. Eastern Interconnection for Weather Year 2018
No full textUsing 2018 weather data, we ran PySAM power production simulations for utility-scale PV sites in the U.S. Eastern Interconnection queue. Site IDs, capacities, and locations (counties) were extracted from Lawrence Berkeley National Laboratory\u27s Queued Up: 2024 Edition dataset. No panel mount information was provided, so all sites were assumed to be the centers of the installation counties. See queued_site_metadata.csv file for individual site metadat