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Ȩ Ȩ > ¿¬±¸¹®Çå > Çмú´ëȸ ÇÁ·Î½Ãµù > Çѱ¹Á¤º¸Åë½ÅÇÐȸ Çмú´ëȸ > 2018³â Ãá°èÇмú´ëȸ

2018³â Ãá°èÇмú´ëȸ

Current Result Document : 1 / 2   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ÀÚÀ²ÁÖÇàÂ÷ Á¶ÇâÁ¦¾î¸¦ À§ÇÑ CNNÀÇ Àû¿ë
¿µ¹®Á¦¸ñ(English Title) Application of CNN for steering control of autonomous vehicle
ÀúÀÚ(Author) ¹Ú¼ºÂù   Ȳ±¤º¹   ¹ÚÈñ¹®   ÃÖ¿µ±Ô   ¹ÚÁøÇö   Sung-chan Park   Kwang-bok Hwang   Hee-mun Park   Young-kiu Choi   Jin-hyun Park  
¿ø¹®¼ö·Ïó(Citation) VOL 22 NO. 01 PP. 0234 ~ 0234 (2018. 05)
Çѱ۳»¿ë
(Korean Abstract)
º» ¿¬±¸´Â ÀÚµ¿Â÷ Á¶ÇâÁ¦¾î ½Ã½ºÅÛ¿¡ Àû¿ë °¡´ÉÇÑ CNN(Convolutional Neural Network)À» ¼³°èÇÏ°íÀÚ ÇÑ´Ù. CNNÀº ÇöÀç ¸¹Àº ºÐ¾ß¿¡¼­ Æø³Ð°Ô »ç¿ëµÇ°í ÀÖÀ¸¸ç, ƯÈ÷ ¿µ»ó ºÐ·ù(image classification) ÀÛ¾÷¿¡ ÀÖ¾î ¸Å¿ì ¶Ù¾î³­ ¼º´ÉÀ» ³ªÅ¸³»°í ÀÖ´Ù. ±×·¯³ª ÀÌ·¯ÇÑ CNNÀÌ ÇÔ¼ö¸¦ ±Ù»çÇϴ ȸ±Í(regression)¹®Á¦¿¡¼­´Â ¸¹ÀÌ Àû¿ëµÇÁö ¸øÇÏ°í ÀÖ´Ù. ÀÌ´Â CNNÀÇ ÀÔ·ÂÀ¸·Î À̹ÌÁö µ¥ÀÌÅÍ¿Í °°Àº ´ÙÂ÷¿øÀûÀÎ µ¥ÀÌÅÍ ±¸Á¶·Î µÇ¾î ÀÖ¾î ÀϹÝÀûÀÎ Á¦¾î ½Ã½ºÅÛÀÇ Àû¿ëÀÌ ½±Áö ¾Ê±â ¶§¹®ÀÌ´Ù. ÃÖ±Ù µé¾î ÀÚÀ²ÁÖÇàÂ÷¿¡ °üÇØ ¿¬±¸°¡ È°¹ßÈ÷ ÁøÇàµÇ°í ÀÖÀ¸¸ç, ÀÚÀ²ÁÖÇàÂ÷¸¦ ±¸ÇöÇϱâ À§ÇØ ¸¹Àº ±â¼úÀÌ ¿ä±¸µÈ´Ù. À̸¦ À§ÇØ Â÷·®¿¡ ÀåÂøµÈ ºí·¢¹Ú½ºÀÇ ¿µ»ó À̹ÌÁö¸¦ »ç¿ëÇÏ¿© Â÷¼±À» °ËÃâÇÏ°í °ËÃâµÈ Â÷¼±¿¡ µû¶ó ¼Ò½ÇÁ¡ µîÀ» °ËÃâÇÏ¿© ÀÚÀ²ÁÖÇàÂ÷¸¦ Á¦¾îÇÏ´Â ¿¬±¸°¡ ¸¹ÀÌ ÁøÇàµÇ¾ú´Ù. ±×·¯³ª ¼Ò½ÇÁ¡ °ËÃâ¿¡ ÀÖ¾î ¿µ»ó À̹ÌÁöÀÇ ¿ÜºÎ ȯ°æ, ¼ø°£ÀûÀÎ Â÷¼±ÀÇ ¼Ò½Ç ±×¸®°í ¹Ý´ëÆí Â÷¼±ÀÇ °ËÃâ µî ¿©·¯ ¿äÀÎÀ¸·Î ÀÎÇÏ¿© ¾ÈÁ¤ÀûÀÎ ¼Ò½ÇÁ¡ °ËÃâ¿¡ ¾î·Á¹«ÀÌ ÀÖ´Ù. º» ¿¬±¸¿¡¼­´Â ÀÚµ¿Â÷¿¡¼­ ÃÔ¿µµÈ ºí·¢¹Ú½º ¿µ»ó À̹ÌÁö¸¦ ÀÔ·Â¹Þ¾Æ ÀÚÀ²ÁÖÇàÂ÷ÀÇ Á¶ÇâÁ¦¾î¸¦ À§ÇØ CNNÀ» Àû¿ëÇØ º¸°íÀÚ ÇÑ´Ù.
¿µ¹®³»¿ë
(English Abstract)
We design CNN(convolutional neural network) which is applicable to steering control system of autonomous vehicle. CNN has been widely used in many fields, especially in image classifications. But CNN has not been applied much to the regression problem such as function approximation. This is because the input of CNN has a multidimensional data structure such as image data, which makes it is not applicable to general control systems. Recently, autonomous vehicles have been actively studied, and many techniques are required to implement autonomous vehicles. For this purpose, many researches have been studied to detect the lane by using the image through the black box mounted on the vehicle, and to get the vanishing point according to the detected lane for control the autonomous vehicle. However, in detecting the vanishing point, it is difficult to detect the vanishing point with stability due to various factors such as the external environment of the image, disappearance of the instant lane and detection of the opposite lane. In this study, we apply CNN for steering control of an autonomous vehicle using a black box image of a car.
Å°¿öµå(Keyword) CNN   regression   vanishing point  
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