Feature Relevance Estimation for Learning Pedestrian Behavior at Crosswalks

For future automated driving functions it is necessary to be able to reason about the typical behavior, intentions and future movements of vulnerable road users in urban traffic scenarios. It is crucial to have this... [ view full abstract ]

For future automated driving functions it is necessary
to be able to reason about the typical behavior, intentions
and future movements of vulnerable road users in urban traffic
scenarios. It is crucial to have this information as early as
possible, given the typical reaction time of human drivers. Since
this is a highly complex problem, it needs to be addressed in
small portions. In this paper we will focus on the behavior of
pedestrians at crosswalks. We use a database of real pedestrian
trajectories to learn a model which is able to predict if a
pedestrian will cross the street. Therefore, we first introduce a
large set of possible features that could be suitable to describe
the behavior. Afterwards, we perform relevance determination
to identify those features that are necessary to reach the best
possible generalisation performance. We provide experimental
results on data collected at a pedestrian crossing in a city in
southern Germany. Our results shows, that a very sparse set of
features, which depends only on the pedestrians’ trajectory, gives
the best result.