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A rapid simulation development platform for autonomous driving based on CARLA and ROS

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DOI: 10.23977/jaip.2023.060303 | Downloads: 13 | Views: 331


Zhou Su 1,2, Zhu Zhenhua 1, Zhu Xiaofeng 1


1 School of Automotive Studies, Tongji University, Shanghai, China
2 Sino-German College, Tongji University, Shanghai, China

Corresponding Author

Zhu Zhenhua


This paper discusses the development of a rapid simulation development platform based on CARLA and ROS. Firstly, the high cost and difficulty of algorithm verification in real-world experiments, mapping, and planning were introduced. The goal of accelerating research and development efficiency through the use of simulation development platforms was proposed. Based on these requirements, a multi-level platform architecture was designed, and the platform architecture, construction process, and related applications were introduced, creating a rapid development platform for autonomous driving simulation tasks. Finally, using mapping experiments and motion planning experiments as examples, the application of the rapid development platform was introduced.


Simulation platform, CARLA, Motion planning, Semantic mapping


Zhou Su, Zhu Zhenhua, Zhu Xiaofeng, A rapid simulation development platform for autonomous driving based on CARLA and ROS. Journal of Artificial Intelligence Practice (2023) Vol. 6: 15-25. DOI:


[1] Qin T, Chen T, Chen Y, et al. Avp-slam: Semantic visual mapping and localization for autonomous vehicles in the parking lot[C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020: 5939-5945.
[2] Koenig N, Howard A. Design and use paradigms for gazebo, an open-source multi-robot simulator[C]//2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566). IEEE, 2004, 3: 2149-2154.
[3] Dosovitskiy A, Ros G, Codevilla F, et al. CARLA: An open urban driving simulator[C]//Conference on robot learning. PMLR, 2017: 1-16.
[4] Shah S, Dey D, Lovett C, et al. Airsim: High-fidelity visual and physical simulation for autonomous vehicles[C]//Field and service robotics. Springer, Cham, 2018: 621-635.
[5] Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The kitti vision benchmark suite[C]//2012 IEEE conference on computer vision and pattern recognition. IEEE, 2012: 3354-3361.
[6] Kam H R, Lee S H, Park T, et al. Rviz: a toolkit for real domain data visualization[J]. Telecommunication Systems, 2015, 60: 337-345.
[7] Gao X, Zhang T, Liu Y, et al. 14 lectures on visual SLAM: from theory to practice[J]. Publishing House of Electronics Industry, Beijing, 2017.
[8] Fan H, Zhu F, Liu C, et al. Baidu apollo em motion planner[J]. arXiv preprint arXiv: 1807. 08048, 2018.
[9] Werling M, Ziegler J, Kammel S, et al. Optimal trajectory generation for dynamic street scenarios in a frenet frame[C]//2010 IEEE International Conference on Robotics and Automation. IEEE, 2010: 987-993.
[10] Xu W, Wei J, Dolan J M, et al. A real-time motion planner with trajectory optimization for autonomous vehicles [C] //2012 IEEE International Conference on Robotics and Automation. IEEE, 2012: 2061-2067.
[11] Takahashi A, Hongo T, Ninomiya Y, et al. Local path planning and motion control for agv in positioning [C] //Proceedings. IEEE/RSJ International Workshop on Intelligent Robots and Systems'. (IROS'89)'The Autonomous Mobile Robots and Its Applications. IEEE, 1989: 392-397. 

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