  In autonomous driving, using a variety of sensors to recognize preceding
vehicles in middle and long distance is helpful for improving driving
performance and developing various functions. However, if only LiDAR or camera
is used in the recognition stage, it is difficult to obtain necessary data due
to the limitations of each sensor. In this paper, we proposed a method of
converting the tracking data of vision into bird's eye view (BEV) coordinates
using an equation that projects LiDAR points onto an image, and a method of
fusion between LiDAR and vision tracked data. Thus, the newly proposed method
was effective through the results of detecting closest in-path vehicle (CIPV)
in various situations. In addition, even when experimenting with the EuroNCAP
autonomous emergency braking (AEB) test protocol using the result of fusion,
AEB performance is improved through improved cognitive performance than when
using only LiDAR. In experimental results, the performance of the proposed
method was proved through actual vehicle tests in various scenarios.
Consequently, it is convincing that the newly proposed sensor fusion method
significantly improves the ACC function in autonomous maneuvering. We expect
that this improvement in perception performance will contribute to improving
the overall stability of ACC.
