Efficient analysis of GPS antenna onboard self-driving car in practical environments with GPU accelerated MoM-PO method

Author(s):  
Zi-Liang Liu ◽  
Chao-Fu Wang
Keyword(s):  
Author(s):  
Mark W. Arndt ◽  
Stephen M. Arndt ◽  
Donald Stevens

A study of numerous published rollover tests was conducted by reexamination of the original works, analysis of their data, and centralized compilation of their results. Instances were identified where the original reported results for trip speed were in error, requiring revision because the analysis technique employed extrapolation versus integration and lacked correction for offset errors that develop by placing the Global Positioning System (GPS) antenna away from the vehicle Center of Gravity (CG). An analysis was performed demonstrating revised results. In total, 81 dolly rollover crash tests, 24 naturally occurring rollover crash tests, and 102 reconstructed rollovers were identified. Of the 24 naturally occurring tests, 18 were steer-induced rollover tests. Distributions of the rollover drag factors are presented. The range of drag factors for all examined dolly rollovers was 0.38 g to 0.50 g with the upper and lower 15 percent statistically trimmed. The average drag factor for dolly rollovers was 0.44 g (standard deviation = 0.064) with a reported minimum of 0.31 g and a reported maximum of 0.61 g. After revisions, the range of drag factors for the set of naturally occurring rollovers was 0.39 g to 0.50 g with the upper and lower 15 percent statistically trimmed. The average drag factor for naturally occurring rollovers was 0.44 g (standard deviation = 0.063) with a reported minimum of 0.33 g and a reported maximum of 0.57 g. These results provide a more probable range of the drag factor for use in accident reconstruction compared to the often repeated assertion that rollover drag factors range between 0.4 g and 0.65 g.


2009 ◽  
Vol 57 (1) ◽  
pp. 33-46 ◽  
Author(s):  
Francesca Scire-Scappuzzo ◽  
Sergey N. Makarov
Keyword(s):  

2018 ◽  
Vol 60 (4) ◽  
pp. 1061-1066 ◽  
Author(s):  
Prasadh Ramachandran ◽  
Hanna Kähäri ◽  
Jari Juuti ◽  
Heli Jantunen

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4994
Author(s):  
Haohan Wei ◽  
Xiufeng He ◽  
Yanming Feng ◽  
Shuanggen Jin ◽  
Fei Shen

Snow is one of the most critical sources of freshwater, which influences the global water cycle and climate change. However, it is difficult to monitor global snow variations with high spatial–temporal resolution using traditional techniques due to their costly and labor-intensive nature. Nowadays, the Global Positioning System Interferometric Reflectometry (GPS-IR) technique can measure the average snow depth around a GPS antenna using its signal-to-noise ratio (SNR) data. Previous studies focused on the use of GPS data at sites located in flat areas or on very gentle slopes. In this contribution, we propose a strategy called the Tilted Surface Strategy (TSS), which uses the SNR data reflected only from the flat quadrants to estimate the snow depth instead of the conventional strategy, which employs all the SNR data reflected from the whole area around a GPS antenna. Three geodetic GPS sites from the Plate Boundary Observatory (PBO) project were chosen in this experimental study, of which GPS sites p683 and p101 were located on slopes with their gradients up to 18% and the site p025 was located on a flat area. Comparing the snow depths derived with the GPS-IR TSS method with the snow depth results provided with the GPS-PBO, i.e., GPS-IR with the conventional strategy, the Snowpack Telemetry (SNOTEL) network measurements and gridded Snow Data Assimilation System (SNODAS) estimates, it was found that the snow depths derived with the four methods had a good agreement, but the snow depth time series with the GPS-IR TSS method were closer to the SNOTEL measurements and the SNODAS estimates than those with GPS-PBO method. Similar observations were also obtained from the cumulative snowfall time series. Results generally indicated that for those GPS sites located on slopes, the TSS strategy works better.


2011 ◽  
Vol 10 ◽  
pp. 266-269 ◽  
Author(s):  
Jae-Hoon Bang ◽  
B Enkhbayar ◽  
Dong-Hyun Min ◽  
Bierng-Chearl Ahn

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