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Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction | IEEE Conference Publication | IEEE Xplore

Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction


Abstract:

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive prediction and plan-ning framework that can enable more efficient and less conse...Show More

Abstract:

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive prediction and plan-ning framework that can enable more efficient and less conser-vative maneuvers in interactive scenarios. In CBP task, we train a prediction model approximating the posterior distribution of target agents' future trajectories conditioned on the future trajectory of an assigned ego agent. However, we argue that CBP may provide overly confident anticipation on how the autonomous agent may influence the target agents' behavior. Consequently, it is risky for the planner to query a CBP model. Instead, we should treat the planned trajectory as an intervention and let the model learn the trajectory distribution under intervention. We refer to it as the interventional behavior prediction (IBP) task. Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution. We show that the proposed metric can effectively identify a CBP model violating the temporal independence, which plays an important role when establishing IBP benchmarks.
Date of Conference: 23-27 October 2022
Date Added to IEEE Xplore: 26 December 2022
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Conference Location: Kyoto, Japan

References

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