This paper concerns a new methodology for accuracy assessment of GPS (Global Positioning System) verified experimentally with LiDAR (Light Detection and Ranging) data alignment at continent scale for autonomous driving safety analysis. Accuracy of an autonomous driving vehicle positioning within a lane on the road is one of the key safety considerations and the main focus of this paper. The accuracy of GPS positioning is checked by comparing it with mobile mapping tracks in the recorded high-definition source. The aim of the comparison is to see if the GPS positioning remains accurate up to the dimensions of the lane where the vehicle is driving. The goal is to align all the available LiDAR car trajectories to confirm the of accuracy of GNSS + INS (Global Navigation Satellite System + Inertial Navigation System). For this reason, the use of LiDAR metric measurements for data alignment implemented using SLAM (Simultaneous Localization and Mapping) was investigated, assuring no systematic drift by applying GNSS+INS constraints. The methodology was verified experimentally using arbitrarily chosen measurement instruments (NovAtel GNSS + INS, Velodyne HDL32 LiDAR) mounted onto mobile mapping systems. The accuracy was assessed and confirmed by the alignment of 32,785 trajectories with a total length of 1,159,956.9 km and a total of 186.4 × 109 optimized parameters (six degrees of freedom of poses) that cover the United States region in the 2016-2019 period. The alignment improves the trajectories; thus the final map is consistent. The proposed methodology extends the existing methods of global positioning system accuracy assessment, focusing on realistic environmental and driving conditions. The impact of global positioning system accuracy on autonomous car safety is discussed. It is shown that 99% of the assessed data satisfy the safety requirements (driving within lanes of 3.6 m) for Mid-Size (width 1.85 m, length 4.87 m) vehicles and 95% for Six-Wheel Pickup (width 2.03-2.43 m, length 5.32-6.76 m). The conclusion is that this methodology has great potential for global positioning accuracy assessment at the global scale for autonomous driving applications. LiDAR data alignment is introduced as a novel approach to GNSS + INS accuracy confirmation. Further research is needed to solve the identified challenges.
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http://dx.doi.org/10.3390/s21175691 | DOI Listing |
J Mol Biol
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Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Electronic address:
N4-acetylcytidine (ac4C) is a crucial post-transcriptional modification in human mRNA, involving the acetylation of the nitrogen atom at the fourth position of cytidine. This modification, catalyzed by N-acetyltransferases such as NAT10, is primarily found in mRNA's coding regions and enhances translation efficiency and mRNA stability. ac4C is closely associated with various diseases, including cancer.
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Museum of Nature Hamburg - Zoology, Leibniz Institute for the Analysis of Biodiversity Change (LIB) and University Hamburg, Martin-Luther-King-Platz 3 20146 Hamburg, Germany. Electronic address:
Priapulida is a small phylum of 22 described species that are divided into two size classes (microscopic and macroscopic), distinguished by adult and larval morphology. Most priapulidans are rare or live in inaccessible habitats, and freshly collected material for molecular studies is difficult to obtain. With this study, we for the first time aim to resolve the phylogeny of extant Priapulida using transcriptomic, genomic, and morphological data.
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Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden. Electronic address:
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PLoS One
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Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao, Liaoning, China.
To address the susceptibility of conventional vector control systems for permanent magnet synchronous motors (PMSMs) to motor parameter variations and load disturbances, a novel control method combining an improved Grasshopper Optimization Algorithm (GOA) with a variable universe fuzzy Proportional-Integral (PI) controller is proposed, building upon standard fuzzy PI control. First, the diversity of the population and the global exploration capability of the algorithm are enhanced through the integration of the Cauchy mutation strategy and uniform distribution strategy. Subsequently, the fusion of Cauchy mutation and opposition-based learning, along with modifications to the optimal position, further improves the algorithm's ability to escape local optima.
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