TY - GEN
T1 - Recognition of parking spaces on dry and wet road surfaces using received light intensity of laser for ultra small EVs
AU - Kamiyama, Tatsuya
AU - Maeyama, Shoichi
AU - Okawa, Kazuya
AU - Watanabe, Keigo
AU - Nogami, Yasuyuki
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/25
Y1 - 2019/4/25
N2 - Research and development of automatic driving technology has been actively conducted in recent years. Under this background, it is considered that the car parking problem is expected to be developed for supporting drivers to complete automatic parking. Many parking lots in an urban area prepare parking spaces with asphalt road surface drawn by white paint. Research to recognize parking spaces using camera images has been conducted so far. However, camera images are known to be not robust to environmental conditions such as nighttime and a backlight. Therefore, a method of recognizing parking spaces is proposed so that it can be used even when the use time is day or night, irrespective of whether the road surface is dry or wet. In the proposed method, the road surface is classified using the statistical model of the received light intensity value of laser range scanner. Then, the target parking position is estimated by Hough transformation.
AB - Research and development of automatic driving technology has been actively conducted in recent years. Under this background, it is considered that the car parking problem is expected to be developed for supporting drivers to complete automatic parking. Many parking lots in an urban area prepare parking spaces with asphalt road surface drawn by white paint. Research to recognize parking spaces using camera images has been conducted so far. However, camera images are known to be not robust to environmental conditions such as nighttime and a backlight. Therefore, a method of recognizing parking spaces is proposed so that it can be used even when the use time is day or night, irrespective of whether the road surface is dry or wet. In the proposed method, the road surface is classified using the statistical model of the received light intensity value of laser range scanner. Then, the target parking position is estimated by Hough transformation.
UR - http://www.scopus.com/inward/record.url?scp=85065642244&partnerID=8YFLogxK
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U2 - 10.1109/SII.2019.8700344
DO - 10.1109/SII.2019.8700344
M3 - Conference contribution
AN - SCOPUS:85065642244
T3 - Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019
SP - 494
EP - 501
BT - Proceedings of the 2019 IEEE/SICE International Symposium on System Integration, SII 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE/SICE International Symposium on System Integration, SII 2019
Y2 - 14 January 2019 through 16 January 2019
ER -