Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety

Predicting the motion of traffic participants accurately remains a challenging task in the field of automated driving. Especially interactions between traffic participants introduce high complexity and interdependencies into the environment prediction. This work presents the remarkable performance of a Partially Observable Markov Decision Process (POMDP) framework to stochastically predict and safely respond to an interacting environment. The framework is validated for its ability to increase the overall Ego-Vehicle safety by preemptively triggering a de-escalation maneuver. The performance of the framework is analyzed on a publicly available dataset with real-world traffic (Argoverse) and on highly critical simulation scenarios specified by Euro-NCap for emergency braking functions. The results show quantitatively that the proposed framework significantly contributes to an early de-escalation of critical scenarios. Such an early de-escalation increases the safety and comfort of automated vehicles.