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

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arxiv 2311.11058 v3 pith:T4M3DZ5M submitted 2023-11-18 cs.LG

classification cs.LG
keywords tactics2ddrivingscenariostrafficdecision-makingmodelsbehaviordiverse
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline

    cs.CV 2025-04 reject novelty 3.0 of 10

    On a new Waymo-based causal benchmark, an imitation-plus-reinforcement planner (MTR-SAC) reaches goals more often than imitation alone but crashes and goes offroad more, contradicting the claimed safety improvement.

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