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 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 the authors will focus on the behavior of pedestrians at crosswalks. The authors use a database of real pedestrian trajectories to learn a model which is able to predict if a pedestrian will cross the street. Therefore, the authors first introduce a large set of possible features that could be suitable to describe the behavior. Afterwards, the authors perform relevance determination to identify those features that are necessary to reach the best possible generalisation performance. The authors provide experimental results on data collected at a pedestrian crossing in a city in southern Germany. The authors' results shows, that a very sparse set of features, which depends only on the pedestrians' trajectory, gives the best result.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: 854-860
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01615281
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:17PM