Yueyuan Li;Songan Zhang;Mingyang Jiang;Xingyuan Chen;Jing Yang;Yeqiang Qian;Chunxiang Wang;Ming Yang
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引用次数: 0
Abstract
Simulators generate diverse and realistic traffic scenarios to boost the development of autonomous driving systems. However, existing simulators often fall short in scenario diversity and interactive behavior models for traffic participants. This deficiency underscores the need for a flexible, reliable, user-friendly open-source simulator. Addressing this challenge, Tactics2D provides a highly modular and extensive framework for traffic scenario construction, encompassing road elements, traffic regulations, behavior models, physics simulations for vehicles, and event detection mechanisms. By integrating numerous popular algorithms and models, Tactics2D empowers users to customize driving scenarios and evaluate model performance across various scenarios by leveraging both public datasets and user-collected real-world data. This letter results from discussions at several IEEE T-IV's Decentralized and Hybrid Workshops on Scenarios Engineering for Smart Mobility.
期刊介绍：
The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges.
Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.
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