Model-based Decision Making with Imagination for Autonomous Parking. (arXiv:2108.11420v1 [cs.RO])

Autonomous parking technology is a key concept within autonomous driving
research. This paper will propose an imaginative autonomous parking algorithm
to solve issues concerned with parking. The proposed algorithm consists of
three parts: an imaginative model for anticipating results before parking, an
improved rapid-exploring random tree (RRT) for planning a feasible trajectory
from a given start point to a parking lot, and a path smoothing module for
optimizing the efficiency of parking tasks. Our algorithm is based on a real
kinematic vehicle model; which makes it more suitable for algorithm application
on real autonomous cars. Furthermore, due to the introduction of the
imagination mechanism, the processing speed of our algorithm is ten times
faster than that of traditional methods, permitting the realization of
real-time planning simultaneously. In order to evaluate the algorithm’s
effectiveness, we have compared our algorithm with traditional RRT, within
three different parking scenarios. Ultimately, results show that our algorithm
is more stable than traditional RRT and performs better in terms of efficiency
and quality.



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