mlsanityData to 14 Oct 2026

Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making

arXiv 2311.11058 ↗First posted 18 Nov 2023cs.LGcited by 5

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Abstract (arXiv)

Simulation is a prospective method for generating diverse and realistic traffic scenarios to aid in the development of driving decision-making systems. However, existing simulators often fall short in diverse scenarios or interactive behavior models for traffic participants. This deficiency underscores the need for a flexible, reliable, user-friendly open-source simulator. Addressing this challenge, Tactics2D adopts a modular approach to traffic scenario construction, encompassing road elements, traffic regulations, behavior models, physics simulations for vehicles, and event detection mechanisms. By integrating numerous commonly utilized algorithms and configurations, Tactics2D empowers users to construct their driving scenarios effortlessly, just like assembling building blocks. Users can effectively evaluate the performance of driving decision-making models across various scenarios by leveraging both public datasets and user-collected real-world data. For access to the source code and community support, please visit the official GitHub page for Tactics2D at https://github.com/WoodOxen/Tactics2D.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 18 Nov 2023.

Builds on

16 of 33 references.

References · 16

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
  4. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
  5. 2021NuPlan: A closed-loop ML-based planning benchmark for autonomous vehiclescited by 493
  6. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learningcited by 290
  7. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 277
  8. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  9. 2023Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Researchcited by 160
  10. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  11. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  12. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 114
4 more
  1. 2022BITS: Bi-level Imitation for Traffic Simulationcited by 80
  2. 2022InterSim: Interactive Traffic Simulation via Explicit Relation Modelingcited by 27
  3. 2023LimSim: A Long-term Interactive Multi-scenario Traffic Simulatorcited by 13
  4. 2020ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lotscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.