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10.3390/s150614661 http://scihub22266oqcxt.onion/10.3390/s150614661 C4507681!4507681!26102493     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sensors+(Basel) 2015 ; 15 (6): 14661-78 Nephropedia Template TP {{tp|p=26102493|t=2015. Radar Sensing for Intelligent Vehicles in Urban Environments |pdf=|usr=}}{{26102493}} gab.com Text Radar Sensing for Intelligent Vehicles in Urban Environments JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26102493 #FOAvip Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations. Twit Text FOAVip Radar Sensing for Intelligent Vehicles in Urban Environments ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=26102493 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26102493 #FOAvip English Wikipedia <ref>{{Cite pmid|26102493}}</ref> Radar Sensing for Intelligent Vehicles in Urban Environments #MMPMID26102493 Reina G; Johnson D; Underwood J Sensors (Basel) 2015[Jun]; 15 (6): 14661-78 PMID26102493show ga Radar overcomes the shortcomings of laser, stereovision, and sonar because it can operate successfully in dusty, foggy, blizzard-blinding, and poorly lit scenarios. This paper presents a novel method for ground and obstacle segmentation based on radar sensing. The algorithm operates directly in the sensor frame, without the need for a separate synchronised navigation source, calibration parameters describing the location of the radar in the vehicle frame, or the geometric restrictions made in the previous main method in the field. Experimental results are presented in various urban scenarios to validate this approach, showing its potential applicability for advanced driving assistance systems and autonomous vehicle operations. äRadar Sensing for Intelligent Vehicles in Urban Environments (epmc).pdf DeepDyve Pubget Overpricing