Comparison of Markov chain abstraction and Monte Carlo simulation for the safety assessment of autonomous cars

Matthias Althoff, Alexander Mergel

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


The probabilistic prediction of road traffic scenarios is addressed. One result is a probabilistic occupancy of traffic participants, and the other result is the collision risk for autonomous vehicles when executing a planned maneuver. The probabilistic occupancy of surrounding traffic participants helps to plan the maneuver of an autonomous vehicle, whereas the computed collision risk helps to decide if a planned maneuver should be executed. Two methods for the probabilistic prediction are presented and compared: 1) Markov chain abstraction and 2) Monte Carlo simulation. The performance of both methods is evaluated with respect to the prediction of the probabilistic occupancy and the collision risk. For each comparison test, we use the same models that generate the probabilistic behavior of traffic participants, where the generation of these data is not compared with real-world data. However, the results independently show the behavior generation that Markov chains are preferred for the probabilistic occupancy, whereas Monte Carlo simulation is clearly preferred for determining the collision risk.

Original languageEnglish
Article number5875884
Pages (from-to)1237-1247
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
StatePublished - Dec 2011
Externally publishedYes


  • Autonomous cars
  • Markov chains
  • Monte Carlo simulation
  • behavior prediction
  • crash probability
  • probabilistic occupancy
  • safety assessment
  • threat level


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