RetroMotion: Retrocausal Motion Forecasting Models are Instructable

2025-05-26

AI Summary

This work addresses multi-agent motion prediction for autonomous driving, jointly modeling both marginal trajectory distributions of individual agents and joint trajectory distributions of interacting agents. To tackle modeling challenges arising from scene constraints and complex agent interactions, we propose a novel multi-task learning framework incorporating inverse causal information flow. Specifically, we introduce a trajectory-level inverse causal information propagation mechanism that enables goal-directed and scene-adaptive directional instruction injection. Built upon a Transformer architecture, our method models positional uncertainty via a compressed exponential power distribution and synergistically generates joint distributions through marginal trajectory re-encoding and pairwise joint modeling. Our approach achieves state-of-the-art performance on Waymo Interaction Prediction and demonstrates strong generalization on Argoverse 2. The implementation is publicly available.

Technology Category

Multiagent Systems: Adversarial AgentsHumans and AI: Human-Aware Planning and Behavior PredictionMachine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingResponsible Web: Machine-in-the-loop, human agency and autonomySemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Motion forecasts of road users (i.e., agents) vary in complexity as a function of scene constraints and interactive behavior. We address this with a multi-task learning method for motion forecasting that includes a retrocausal flow of information. The corresponding tasks are to forecast (1) marginal trajectory distributions for all modeled agents and (2) joint trajectory distributions for interacting agents. Using a transformer model, we generate the joint distributions by re-encoding marginal distributions followed by pairwise modeling. This incorporates a retrocausal flow of information from later points in marginal trajectories to earlier points in joint trajectories. Per trajectory point, we model positional uncertainty using compressed exponential power distributions. Notably, our method achieves state-of-the-art results in the Waymo Interaction Prediction dataset and generalizes well to the Argoverse 2 dataset. Additionally, our method provides an interface for issuing instructions through trajectory modifications. Our experiments show that regular training of motion forecasting leads to the ability to follow goal-based instructions and to adapt basic directional instructions to the scene context. Code: https://github.com/kit-mrt/future-motion

Problem

• Forecasting marginal and joint trajectory distributions for road agents
• Incorporating retrocausal information flow in motion prediction
• Enabling instruction-based trajectory modifications for adaptable forecasting

Innovation

• Multi-task learning with retrocausal information flow
• Transformer model for joint trajectory distributions
• Compressed exponential power for positional uncertainty