A probabilistic framework for collision probability estimation and an analysis of the discretization precision
Paper i proceeding, 2019

This paper presents a probabilistic framework for collision probability estimation. The framework uses information about objects' velocity and acceleration gathered from a larger real traffic data set in order to create a discrete Markov Chain model. This model is then used to predict other traffic participants motion in a given scenario and through this calculate the probability of a future collision. The framework is then analyzed with respect to potential errors that are created in the discretization process. Especially the errors related to the discrete velocity regions are investigated in more detail. The analysis is performed on a selection of critical scenarios from a larger data set in order to set scenario-based requirements of the state discretization resolution. In the end, there is a discussion about the implications for the collision probability estimate, as well as, suggested next steps in order to get a complete view of the precision of the estimate.


Daniel Åsljung

Zenuity AB

Mathias Westlund

Zenuity AB

Jonas Fredriksson

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Intelligent Vehicles Symposium, Proceedings

Vol. 2019-June 52-57 8813853
978-1-7281-0560-4 (ISBN)

30th IEEE Intelligent Vehicles Symposium, IV 2019
Paris, France,


Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik



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