CommonRoad-Game: A Human-in-the-Loop Simulation Framework for Autonomous Driving

• URL: http://arxiv.org/abs/2607.01382v1
• Date: Wed, 01 Jul 2026 18:42:00 GMT
• Title: CommonRoad-Game: A Human-in-the-Loop Simulation Framework for Autonomous Driving
• Abstract summary: We present CommonRoad-Game, a lightweight human-in-the-loop simulation framework tightly integrated with the CommonRoad platform.<n>We focus on the systematic testing of motion planners with human participation and the analysis of human driving behaviors in interactive scenarios.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion planning algorithms should be evaluated in human-in-the-loop environments to ensure they produce safe and efficient behaviors during interactions. However, existing simulation platforms often rely on recorded datasets, lack dedicated interfaces for real-time human interaction, or remain weakly integrated with an autonomous driving ecosystem. Moreover, many human-in-the-loop simulators are computationally intensive by design, making them less suitable for rapid prototyping and flexible experimentation in early-stage autonomous driving research. To address these limitations, we present CommonRoad-Game, a lightweight human-in-the-loop simulation framework tightly integrated with the CommonRoad platform, focusing on the systematic testing of motion planners with human participation and the analysis of human driving behaviors in interactive scenarios. We introduce a multi-threaded architecture with a robust synchronization mechanism that aligns simulation time with wall-clock time, enabling deterministic and temporally consistent interaction between autonomous and human-driven vehicles. In addition, the framework provides a scenario generation module that records driving logs, allowing diverse and reproducible test cases to be constructed from human-in-the-loop experiments. Experimental results demonstrate that CommonRoad-Game achieves stable temporal synchronization, supports scalable multi-agent simulation, and seamlessly integrates CommonRoad-compatible motion planners to generate interactive driving scenarios. The source code is publicly available at https://github.com/Yunfei-Bi8/CommonRoad-Game.

Related papers

• A Real-Time Generalized Nash Equilibrium Framework for Interaction-Aware Autonomous Driving in Mixed Traffic [0.2073116708774899]
  This paper introduces a comprehensive decision-making framework that formulates as a Nash Generalized Equilibrium Problem (GNEP)<n>To solve this problem in real-time, we propose a dedicated solver based on shared Particle Swarm Optimization (PSO)<n> Experimental results demonstrate the system's ability to handle critical scenarios by generating comfortable, human-like trajectories.<n> Benchmarks confirm the solver's operational feasibility, achieving convergence in under 50s m.
  arXiv  Detail & Related papers  (2026-07-23T08:25:01Z)
• LOTUSim: Multi-Domain Simulator for Marine Robotics [0.49838563301861205]
  LOTUSim is a real-time maritime simulator supporting multi-user interaction across aerial, surface, and underwater robotic systems for coordinated naval-style operations.<n> validation demonstrates robust human-in-the-loop performance, maintaining strict real-time execution and high visual fidelity while scaling to large heterogeneous maritime drone swarms.<n>Results confirm LOTUSim's suitability as a simulation platform for operatorin-the-loop maritime robotics research.
  arXiv  Detail & Related papers  (2026-07-03T08:09:01Z)
• NavIsaacLab: Generating Realistic Crowd via Parallel Robot Learning for Benchmarking Human-aware Navigation [40.511983590273445]
  This paper presents NavIsaacLab, a comprehensive framework for benchmarking and training human-aware navigation policies.<n>Based on Isaac Lab, the proposed framework employs photo-realistic scene rendering capabilities and supports parallel simulation on GPU.<n>To enhance the realism of human behavior, a data-driven approach is employed that incorporates a trajectory diffusion model and an adversarial motion learning controller.
  arXiv  Detail & Related papers  (2026-06-24T18:08:19Z)
• GROVE: Grounded Pedestrian Simulation via Natural Language for Interactive Social Robot Navigation [3.066376644627784]
  Pedestrian simulation is a critical component for training and deploying social robot navigation approaches.<n>GROVE is a text-to-scenario pedestrian simulation framework that combines state-of-the-art approaches to produce realistic, socially challenging scenarios.<n>The human simulation is directly integrated into Isaac Sim, Gazebo, and RViz simulators for robot deployment in highly social environments.
  arXiv  Detail & Related papers  (2026-06-24T07:39:01Z)
• From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving [12.14436913195805]
  This work presents a test framework for automotive software on the centralized E/E architecture.<n>The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment.
  arXiv  Detail & Related papers  (2026-03-05T15:23:28Z)
• ForSim: Stepwise Forward Simulation for Traffic Policy Fine-Tuning [13.668397738433852]
  ForSim is a stepwise closed-loop forward simulation paradigm.<n>It preserves multimodal behavioral diversity while ensuring intra-modality consistency.<n>ForSim operates in conjunction with group-relative optimization to fine-tune traffic policy.
  arXiv  Detail & Related papers  (2026-02-02T10:20:11Z)
• HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving [59.55918581964678]
  HybridWorldSim is a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents.<n>We release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities.
  arXiv  Detail & Related papers  (2025-11-27T07:53:16Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• 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)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• 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.