The lack of generalization in learning-based autonomous driving applications
is shown by the narrow range of road scenarios that vehicles can currently
cover. A generalizable approach should capture many distinct road structures
and topologies, as well as consider traffic participants, and dynamic changes
in the environment, so that vehicles can navigate and perform motion planning
tasks even in the most difficult situations. Designing suitable feature spaces
for neural network-based motion planers that encapsulate all kinds of road
scenarios is still an open research challenge. This paper tackles this
learning-based generalization challenge and shows how graph representations of
road networks can be leveraged by using multidimensional scaling (MDS)
techniques in order to obtain such feature spaces. State-of-the-art graph
representations and MDS approaches are analyzed for the autonomous driving use
case. Finally, the option of embedding graph nodes is discussed in order to
perform easier learning procedures and obtain dimensionality reduction.

Cet article explore les excursions dans le temps et leurs implications.

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