  In unstructured environments like parking lots or construction sites, due to
the large search-space and kinodynamic constraints of the vehicle, it is
challenging to achieve real-time planning. Several state-of-the-art planners
utilize heuristic search-based algorithms. However, they heavily rely on the
quality of the single heuristic function, used to guide the search. Therefore,
they are not capable to achieve reasonable computational performance, resulting
in unnecessary delays in the response of the vehicle. In this work, we are
adopting a Multi-Heuristic Search approach, that enables the use of multiple
heuristic functions and their individual advantages to capture different
complexities of a given search space. Based on our knowledge, this approach was
not used previously for this problem. For this purpose, multiple admissible and
non-admissible heuristic functions are defined, the original Multi-Heuristic A*
Search was extended for bidirectional use and dealing with hybrid
continuous-discrete search space, and a mechanism for adapting scale of motion
primitives is introduced. To demonstrate the advantage, the Multi-Heuristic A*
algorithm is benchmarked against a very popular heuristic search-based
algorithm, Hybrid A*. The Multi-Heuristic A* algorithm outperformed baseline in
both terms, computation efficiency and motion plan (path) quality.
