# Enactor: From Traffic Simulators to Surrogate World Models

arXiv: https://arxiv.org/abs/2603.18266 · HTML: https://mlsanity.com/paper/2603.18266/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding](https://mlsanity.com/paper/2205.09753/) (2022)
- [The Waymo Open Sim Agents Challenge](https://mlsanity.com/paper/2305.12032/) (2023)
- [World Models for Autonomous Driving: An Initial Survey](https://mlsanity.com/paper/2403.02622/) (2024)
- [TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction](https://mlsanity.com/paper/2303.04116/) (2023)
- [SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model](https://mlsanity.com/paper/2506.21976/) (2025)
- [Building Transportation Foundation Model via Generative Graph Transformer](https://mlsanity.com/paper/2305.14826/) (2023)
- [TransWorldNG: Traffic Simulation via Foundation Model](https://mlsanity.com/paper/2305.15743/) (2023)
- [Relative Position Matters: Trajectory Prediction and Planning with Polar Representation](https://mlsanity.com/paper/2508.11492/) (2025)
- [IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersections](https://mlsanity.com/paper/2506.08957/) (2025)
- [Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics](https://mlsanity.com/paper/2506.08963/) (2025)
- [Beyond Simulation: Benchmarking World Models for Planning and Causality in Autonomous Driving](https://mlsanity.com/paper/2508.01922/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Generative Model for Closed-Loop Microsimulation of Signalized Intersections](https://mlsanity.com/paper/2606.23588/) (2026)
- [PhaseShift: Topology-Aware Data Harmonization and Model Consolidation Across Signalized Intersections](https://mlsanity.com/paper/2608.25275/) (2026)

## Abstract (arXiv)

Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the behavior models controlling the actions of the actors are overly simplistic and fails to capture realistic actor-actor interactions. Deep learning-based methods have been applied to model vehicles and pedestrians as ``agents" responding to their surrounding ``environment" (including lanes, signals, and neighboring agents). Although effective in learning actor-actor interaction, these approaches fail to generate physically consistent trajectories over long time periods, and they do not explicitly address the complex dynamics that arise at traffic intersections which is a critical location in urban networks. Inspired by the World Model paradigm, we have developed an actor centric generative model using transformer-based architecture that is able to capture the actor-actor interaction, at the same time understanding the geometry to the traffic intersection to generate physically grounded trajectories that are based on learned behavior. Moreover, we test the model in a live ``simulation-in-the-loop" setting, where we generate the initial conditions of the actors using SUMO and then let the model control the dynamics of the actors. We let the simulation run for 40000 timesteps (4000 seconds), testing the performance of the model on long timerange and evaluating the trajectories on traffic engineering related metrics. Experimental results demonstrate that the proposed framework effectively captures complex actor-actor interactions and generates long-horizon, physically consistent trajectories, while requiring significantly fewer training samples than traditional agent-centric generative approaches. Our model is able to outperform the baseline in traffic related as well as aggregate metrics where our model beats the baseline by more than 10x on the KL-Divergence.