Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Safe learning-based optimal motion planning for automated driving   This paper presents preliminary work on learning the search heuristic for the
optimal motion planning for automated driving in urban traffic. Previous work
considered search-based optimal motion planning framework (SBOMP) that utilized
numerical or model-based heuristics that did not consider dynamic obstacles.
Optimal solution was still guaranteed since dynamic obstacles can only increase
the cost. However, significant variations in the search efficiency are observed
depending whether dynamic obstacles are present or not. This paper introduces
machine learning (ML) based heuristic that takes into account dynamic
obstacles, thus adding to the performance consistency for achieving real-time
implementation.

32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ