Using Context to Identify Difficult Driving Situations in Unstructured Environments

  • Kevin R. Dixon
  • Justin D. Basilico
  • Chris Forsythe
  • Wilhelm E. Kincses
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5638)


We present a context-based machine-learning approach for identifying difficult driving situations using sensor data that is readily available in commercial vehicles. The goal of this system is improve vehicle safety by alerting drivers to potentially dangerous situations. The context-based approach is a two-step learning process by first performing unsupervised learning to discover meaningful regularities, or “contexts,” in the vehicle data and then performing supervised learning, mapping the current context to a measure of driving difficulty. To validate the benefit of this approach, we collected driving data from a set of experiments involving both on-road and off-road driving tasks in unstructured environments. We demonstrate that context recognition greatly improves the performance of identifying difficult driving situations and show that the driving-difficulty system achieves a human level of performance on cross-validation data.


Area Under Curve Commercial Vehicle Controller Area Network Unstructured Environment Vehicle Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kevin R. Dixon
    • 1
  • Justin D. Basilico
    • 1
  • Chris Forsythe
    • 1
  • Wilhelm E. Kincses
    • 2
  1. 1.Sandia National LaboratoriesAlbuquerqueUSA
  2. 2.Daimler AG Group ResearchSildelfingenGermany

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