Dynamic collision risk modeling under uncertainty

F. Belkhouche, B. Bendjilali

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


This paper introduces a probabilistic model for collision risk assessment between moving vehicles. The uncertainties in the states and the geometric variables obtained from the sensory system are characterized by probability density functions. Given the states and their uncertainties, the goal is to determine the probability of collision in a dynamic environment. Two approaches are discussed: (1) The virtual configuration space (VCS), and (2) the rates of change of the visibility angles. The VCS is a transformation of observer that reduces collision detection with a moving object to collision detection with a stationary object. This approach allows to create simple geometric collision cones. Error propagation models are used to solve the problem when going from the VCS to the configuration space. The second approach derives the collision conditions in terms of the rate of change of the limit visibility angles. The probability of collision is then calculated. A comparison between the two methods is carried out. Results are illustrated using simulation, including Monte Carlo simulation.

Original languageEnglish (US)
Pages (from-to)525-537
Number of pages13
Issue number4
StatePublished - Jul 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • General Mathematics
  • Computer Science Applications


Dive into the research topics of 'Dynamic collision risk modeling under uncertainty'. Together they form a unique fingerprint.

Cite this