Enactor: From Traffic Simulators to Surrogate World Models

• URL: http://arxiv.org/abs/2603.18266v1
• Date: Wed, 18 Mar 2026 20:38:28 GMT
• Title: Enactor: From Traffic Simulators to Surrogate World Models
• Abstract summary: Traffic microsimulators are widely used to evaluate road network performance under various what-if" conditions.<n>Deep learning-based methods have been applied to model vehicles and pedestrians as agents" responding to their surrounding environment.<n>Inspired by the World Model paradigm, we have developed an actor centric generative model using transformer-based architecture.
• Score: 8.361095816227488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Extended Field of View Analysis for VideoGAN-based Trajectory Generation [2.7474251089367363]
  We build upon previous work on generative adversarial network (GAN)-based semantic bird's-eye-view traffic generation.<n>We improve the semantic representation, replace the trajectory extraction procedure with a graph-based association method.<n>We also introduce a quantitative evaluation framework to assess hallucinations and object permanence in generated videos.
  arXiv  Detail & Related papers  (2026-08-03T14:21:07Z)
• Long-term Traffic Simulation via Structured Autoregressive Modeling [2.8456699777487215]
  We introduce RosettaSim, a unified framework that projects scene topology, agent states, and spawning intents into a structured autoregressive stream with variable length.<n>We also introduce Retrieval-based Traffic Evaluation (RTE), which retrieves semantically similar real-world scenarios as context-aware reference anchors.
  arXiv  Detail & Related papers  (2026-06-30T06:41:28Z)
• Toward Efficient and Robust Behavior Models for Multi-Agent Driving Simulation [4.713158796503602]
  Multi-agent driving simulation requires realistic and computationally efficient behavior models.<n>We adopt an instance-centric scene representation, where each traffic participant and element map is modeled in its own local coordinate frame.<n>To model interactions, we employ a query-centric symmetric context encoder with relative positional encodings between local frames.
  arXiv  Detail & Related papers  (2025-12-05T15:32:36Z)
• SPACeR: Self-Play Anchoring with Centralized Reference Models [50.55045557371374]
  Sim agent policies are realistic, human-like, fast, and scalable in multi-agent settings.<n>Recent progress in imitation learning with large diffusion-based or tokenized models has shown that behaviors can be captured directly from human driving data.<n>We propose SPACeR, a framework that leverages a pretrained tokenized autoregressive motion model as a central reference policy.
  arXiv  Detail & Related papers  (2025-10-20T19:53:02Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models [10.057312592344507]
  We propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents in a scene. SceneDM achieves state-of-the-art results on the Sim Agents Benchmark.
  arXiv  Detail & Related papers  (2023-11-27T11:39:27Z)
• Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding [121.08841110022607]
  Existing agent-centric methods have demonstrated outstanding performance on public benchmarks. We introduce the K-nearest neighbor attention with relative pose encoding (KNARPE), a novel attention mechanism allowing the pairwise-relative representation to be used by Transformers. By sharing contexts among agents and reusing the unchanged contexts, our approach is as efficient as scene-centric methods, while performing on par with state-of-the-art agent-centric methods.
  arXiv  Detail & Related papers  (2023-10-19T17:59:01Z)
• TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction [149.5716746789134]
  We show data-driven traffic simulation can be formulated as a world model. We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving. Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
  arXiv  Detail & Related papers  (2023-03-07T18:28:41Z)
• Objective-aware Traffic Simulation via Inverse Reinforcement Learning [31.26257563160961]
  We formulate traffic simulation as an inverse reinforcement learning problem. We propose a parameter sharing adversarial inverse reinforcement learning model for dynamics-robust simulation learning. Our proposed model is able to imitate a vehicle's trajectories in the real world while simultaneously recovering the reward function.
  arXiv  Detail & Related papers  (2021-05-20T07:26:34Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors [74.67698916175614]
  We propose TrafficSim, a multi-agent behavior model for realistic traffic simulation. In particular, we leverage an implicit latent variable model to parameterize a joint actor policy. We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
  arXiv  Detail & Related papers  (2021-01-17T00:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.