Stochastic Modelling of Autonomous Vehicles Driving Scenarios Using PEPA.

Autonomous vehicles perceive the environment with different kinds of sensors (camera, radar, lidar...). They must evolve in an unpredictable environment and a wide context of dynamic execution, with strong interactions. Therefore, ensuring the functionality and safety of the autonomous driving system has become one of the focuses of research in the field. In order to guarantee the safety of the autonomous vehicle, its occupants and the others road users, it is necessary to validate the decisions of the algorithms for all the situations that will be met by the vehicle. These situations are described and generated as different scenarios. The main objective of this work is to generate all these scenarios and find out the critical ones. Therefore, we use a scenario-generation methodology which uses the Performance Evaluation Process Algebra (PEPA) for modelling the transitions between the driving scenes. To apply our approach, we consider a running example about a riding autonomous vehicle in the context of a three-lane highway.