Unified automotive safety technology is the tendency of developing automotive safety technologies. The occupant protection performance of conventional automotive passive safety technology can be further improved by cooperating with active sensors such as millimeter-wave radars and cameras. The view of interest in automotive passive safety technology is nearer than that in active safety technology. A calibration method for radars and cameras was developed using vehicle longitudinal symmetry plane as a datum to more accurately detect and locate in automotive passive safety application. Calibration parameters were obtained, with the coordinate transformation relationship between the two sensors being developed. Validation tests show that the radar and the camera in this method have good measurement accuracy, and the target location can be transformed into picture coordinate accurately. This method can provide reliable data for automotive safety systems.
Gietelink O. Design and validation of advanced driver assistance system [D]. Delft: Delft University of Technology, 2007.
[2]
Lemmen P, Fagerlind H, Unselt T, et al.Assessment of integrated vehicle safety systems for improved vehicle safety[J]. Procedia-Social and Behavioral Sciences, 2012, 48: 1632-1641.
[3]
Pietzsch S, Vu T, Burlet J, et al.Results of a precrash application based on laser scanner and short-range radars[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(4): 584-593.
[4]
Bertolazzi E, Biral F, Da L, et al.Supporting drivers in keeping safe speed and safe distance: The SASPENCE subproject within the European framework programme 6 integrating project PReVENT[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3): 525-538.
[5]
Alessandretti G, Broggi A, Cerri P. Vehicle and guard rail detection using radar and vision data fusion[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(1): 95-105.
[6]
JI Zhengping, Luciw M, WENG Juyang, et al.Incremental online object learningin a vehicular radar radar-vision fusion framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(2): 402-411.
[7]
郭磊, 刘志峰, 王建强, 等. 雷达与机器视觉的空间同步方法[J]. 清华大学学报: 自然科学版, 2006, 46(11): 1904-1907. GUO Lei, LIU Zhifeng, WANG Jianqiang, et al.Spatial synchronization method for lidar and computer vision[J]. J Tsinghua Univ: Sci and Technol, 2006, 46(11): 1904-1907. (in Chinese)
[8]
GAO Dezhi, DUAN Jianmin, YANG Xining, et al.A method of spatial calibration for camera and radar [C]// Proceedings of the World Congress on Intelligent Control and Automation. Jinan, China: IEEE Press, 2010: 6211-6215.
[9]
Sugimoto S, Tateda H, Takahashi H, et al.Obstacle detection using millimeter-wave radar and its visualization on image sequence [C]// Proceedings of the 17th International Conference of Pattern Recognition. Cambridge, UK: IEEE Press, 2004: 342-345.
[10]
LIU Xianru, CAI Zixing. Advanced obstacles detection and tracking by fusing millimeter wave radar and image sensor data [C]// International Conference on Control, Automation and Systems. Gyeonggi-do, Korea: IEEE Press, 2010: 1115-1120.
[11]
WANG Tao, ZHENG Nanning, XIN Jingmin, et al.Integrating millimeter wave radar with a monocular vision sensor for on-road obstacle detection applications[J]. Sensors, 2011, 11(9): 8992-9008.
[12]
ZHANG Zhengyou. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[13]
Southhall B, Taylor C. Stochastic road shape estimation [C]// Proceedings of the IEEE International Conference on Computer Vision. Vancouver: IEEE Press, 2001: 205-212.
2014, Vol. 54 
)
