# Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction

arXiv: https://arxiv.org/abs/2204.08665 · HTML: https://mlsanity.com/paper/2204.08665/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings](https://mlsanity.com/paper/1905.01296/) (2019)
- [Multiple Futures Prediction](https://mlsanity.com/paper/1911.00997/) (2019)
- [Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?](https://mlsanity.com/paper/2006.14911/) (2020)
- [LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting](https://mlsanity.com/paper/2101.06653/) (2021)
- [PiP: Planning-informed Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2003.11476/) (2020)
- [What-If Motion Prediction for Autonomous Driving](https://mlsanity.com/paper/2008.10587/) (2020)
- [Learning to Predict Vehicle Trajectories with Model-based Planning](https://mlsanity.com/paper/2103.04027/) (2021)
- [Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction](https://mlsanity.com/paper/1710.09483/) (2017)
- [Identifying Driver Interactions via Conditional Behavior Prediction](https://mlsanity.com/paper/2104.09959/) (2021)
- [You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction](https://mlsanity.com/paper/2110.05304/) (2021)
- [Deep Structured Reactive Planning](https://mlsanity.com/paper/2101.06832/) (2021)
- [DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution](https://mlsanity.com/paper/2106.14160/) (2021)
- [Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling](https://mlsanity.com/paper/2112.00298/) (2021)
- [On complementing end-to-end human behavior predictors with planning](https://mlsanity.com/paper/2103.05661/) (2021)

## Built on by (most cited first, 8 of 8)

- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving](https://mlsanity.com/paper/2502.16589/) (2025)
- [Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation](https://mlsanity.com/paper/2303.13830/) (2023)
- [Energy-based Potential Games for Joint Motion Forecasting and Control](https://mlsanity.com/paper/2312.01811/) (2023)
- [Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving](https://mlsanity.com/paper/2302.03939/) (2023)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [On a Connection between Differential Games, Optimal Control, and Energy-based Models for Multi-Agent Interactions](https://mlsanity.com/paper/2308.16539/) (2023)

## Abstract (arXiv)

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive prediction and planning framework that can enable more efficient and less conservative 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.