At the Institute of Automotive Engineering (FZD) at TU Darmstadt, we develop modeling and validation strategies for Automated Driving Systems (ADS) within the framework of the DFG MiRoVA research project. To simulate the operational and tactical driving behavior of varying automation levels and system designs, this thesis investigates the application of stochastic state machines.

Task Details:

• Literature review on state machines, stochastic FSMs, and their application in driving behavior simulation
• Design and implementation of a stochastic FSM in Python based on an existing master’s thesis
• Parameterization with stochastic uncertainties
• Integration into an existing simulation environment
• Evaluation using various automation designs and ADS variants