Using Context Information and Probabilistic Classification for Making Extended Long-Term Trajectory Predictions

Intersections are among the most accident prone spots in traffic. Future Advanced Driver Assistance Systems (ADAS) are aiming to assist the driving task in these complex scenarios. This can be realized by assessing the criticality of possible occurring situations. For such criticality assessment techniques predicting the trajectories of the involved traffic participants several seconds in advance is necessary. In this paper the authors outline a method that makes exhaustive use of context information to reliably predict maneuver-specific trajectories up to 5 seconds into the future. Since the evolution of traffic scenes cannot be predicted with absolute certainty, approximating future states in form of probability density functions will be of great benefit in terms of robustness and reliability. Since, methods for approximating probability distributions are complex and in most cases computationally inefficient, the authors present an approach that is able to efficiently construct a discrete probability distribution by reformulating the problem as a probabilistic multiclass classification problem. The presented approach is evaluated on a real-world data set containing approaches to 85 different intersections. The authors show that reliable maneuver-specific state estimations can be made, even for a prediction horizon of up to 5 seconds.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 705-711
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01600914
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:21PM