Multi-Fidelity Recursive Behavior Prediction
This addresses the critical problem of behavior prediction for automated driving systems, representing an incremental improvement over existing methods.
The paper tackles predicting vehicle behavior for automated driving by introducing a game-theoretic model that reasons about agent interactions, achieving state-of-the-art accuracy with lower root mean square error on the NGSIM dataset.
Predicting the behavior of surrounding vehicles is a critical problem in automated driving. We present a novel game theoretic behavior prediction model that achieves state of the art prediction accuracy by explicitly reasoning about possible future interaction between agents. We evaluate our approach on the NGSIM vehicle trajectory data set and demonstrate lower root mean square error than state-of-the-art methods.