Trajectory Planning Among Interactive Markovian Obstacles using Scenario Model Predictive Control*

Paper in proceeding, 2025

We propose a scenario model predictive controller (SMPC) for determining the acceleration of an autonomous vehicle (AV) based on decisions made by human-driven vehicle (HDV) obstacles in a traffic intersection. The collective behavior of the HDVs is modeled as a decision process between Markovian agents, and the time-dependent probabilities of scenarios in which the agents decide to occupy the intersection are predicted. The SMPC then determines the AV acceleration that minimizes an expected scenario cost formulated using the transient probability predictions. In simulation, we show how using transient instead of stationary scenario probabilities determines the AV's acceleration based on a trade-off between a current observation and a predicted environment behavior.