Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making
IEEE Transactions on Intelligent Vehicles(2023)
摘要
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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关键词
Simulation,Autonomous Vehicles,Testing,Decision-making
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