# Identifying Driver Interactions via Conditional Behavior Prediction

arXiv: https://arxiv.org/abs/2104.09959 · HTML: https://mlsanity.com/paper/2104.09959/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2021.

## Built on by (most cited first, 30 of 48)

- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction](https://mlsanity.com/paper/2111.14973/) (2021)
- [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023)
- [Scene Transformer: A unified architecture for predicting multiple agent trajectories](https://mlsanity.com/paper/2106.08417/) (2021)
- [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://mlsanity.com/paper/2309.16534/) (2023)
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction](https://mlsanity.com/paper/2202.11884/) (2022)
- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving](https://mlsanity.com/paper/2212.08710/) (2022)
- [Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2212.08787/) (2022)
- [Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts](https://mlsanity.com/paper/2211.02131/) (2022)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting](https://mlsanity.com/paper/2304.03834/) (2023)
- [trajdata: A Unified Interface to Multiple Human Trajectory Datasets](https://mlsanity.com/paper/2307.13924/) (2023)
- [Injecting Planning-Awareness into Prediction and Detection Evaluation](https://mlsanity.com/paper/2110.03270/) (2021)
- [CausalAgents: A Robustness Benchmark for Motion Forecasting using Causal Relationships](https://mlsanity.com/paper/2207.03586/) (2022)
- [MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction](https://mlsanity.com/paper/2209.10033/) (2022)
- [StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving](https://mlsanity.com/paper/2206.00991/) (2022)
- [Rethinking Trajectory Forecasting Evaluation](https://mlsanity.com/paper/2107.10297/) (2021)
- [InteRACT: Transformer Models for Human Intent Prediction Conditioned on Robot Actions](https://mlsanity.com/paper/2311.12943/) (2023)
- [Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation](https://mlsanity.com/paper/2303.13830/) (2023)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction](https://mlsanity.com/paper/2203.15112/) (2022)
- [TIP: Task-Informed Motion Prediction for Intelligent Vehicles](https://mlsanity.com/paper/2110.08750/) (2021)
- [Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction](https://mlsanity.com/paper/2204.08665/) (2022)
- [MoST: Multi-modality Scene Tokenization for Motion Prediction](https://mlsanity.com/paper/2404.19531/) (2024)
- [Learning Interaction-aware Motion Prediction Model for Decision-making in Autonomous Driving](https://mlsanity.com/paper/2302.03939/) (2023)
- [FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding](https://mlsanity.com/paper/2406.14422/) (2024)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)

## Abstract (arXiv)

Interactive driving scenarios, such as lane changes, merges and unprotected turns, are some of the most challenging situations for autonomous driving. Planning in interactive scenarios requires accurately modeling the reactions of other agents to different future actions of the ego agent. We develop end-to-end models for conditional behavior prediction (CBP) that take as an input a query future trajectory for an ego-agent, and predict distributions over future trajectories for other agents conditioned on the query. Leveraging such a model, we develop a general-purpose agent interactivity score derived from probabilistic first principles. The interactivity score allows us to find interesting interactive scenarios for training and evaluating behavior prediction models. We further demonstrate that the proposed score is effective for agent prioritization under computational budget constraints.