  Unstructured environments are difficult for autonomous driving. This is
because various unknown obstacles are lied in drivable space without lanes, and
its width and curvature change widely. In such complex environments, searching
for a path in real-time is difficult. Also, inaccurate localization data reduce
the path tracking accuracy, increasing the risk of collision. Instead of
searching and tracking the path, an alternative approach has been proposed that
reactively avoids obstacles in real-time. Some methods are available for
tracking global path while avoiding obstacles using the candidate paths and the
artificial potential field. However, these methods require heuristics to find
specific parameters for handling various complex environments. In addition, it
is difficult to track the global path accurately in practice because of
inaccurate localization data. If the drivable space is not accurately
recognized (i.e., noisy state), the vehicle may not smoothly drive or may
collide with obstacles. In this study, a method in which the vehicle drives
toward drivable space only using a vision-based occupancy grid map is proposed.
The proposed method uses imitation learning, where a deep neural network is
trained with expert driving data. The network can learn driving patterns suited
for various complex and noisy situations because these situations are contained
in the training data. Experiments with a vehicle in actual parking lots
demonstrated the limitations of general model-based methods and the
effectiveness of the proposed imitation learning method.
