  Detecting vehicles with strong robustness and high efficiency has become one
of the key capabilities of fully autonomous driving cars. This topic has
already been widely studied by GPU-accelerated deep learning approaches using
image sensors and 3D LiDAR, however, few studies seek to address it with a
horizontally mounted 2D laser scanner. 2D laser scanner is equipped on almost
every autonomous vehicle for its superiorities in the field of view, lighting
invariance, high accuracy and relatively low price. In this paper, we propose a
highly efficient search-based L-Shape fitting algorithm for detecting positions
and orientations of vehicles with a 2D laser scanner. Differing from the
approach to formulating LShape fitting as a complex optimization problem, our
method decomposes the L-Shape fitting into two steps: L-Shape vertexes
searching and L-Shape corner localization. Our approach is computationally
efficient due to its minimized complexity. In on-road experiments, our approach
is capable of adapting to various circumstances with high efficiency and
robustness.
