Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.11058 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916250855145472 |
|---|---|
| author | Li, Yueyuan Zhang, Songan Jiang, Mingyang Chen, Xingyuan Qian, Yeqiang Wang, Chunxiang Yang, Ming |
| author_facet | Li, Yueyuan Zhang, Songan Jiang, Mingyang Chen, Xingyuan Qian, Yeqiang Wang, Chunxiang Yang, Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11058 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making Li, Yueyuan Zhang, Songan Jiang, Mingyang Chen, Xingyuan Qian, Yeqiang Wang, Chunxiang Yang, Ming Machine Learning 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. |
| title | Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2311.11058 |