Comparison of Markov Chain Abstraction and Monte Carlo Simulation for the Safety Assessment of Autonomous Cars

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.