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CARLA-Autoware-Bridge: Facilitating Autonomous Driving Research with a Unified Framework for Simulation and Module Development (2402.11239v1)

Published 17 Feb 2024 in cs.RO

Abstract: Extensive testing is necessary to ensure the safety of autonomous driving modules. In addition to component tests, the safety assessment of individual modules also requires a holistic view at system level, which can be carried out efficiently with the help of simulation. Achieving seamless compatibility between a modular software stack and simulation is complex and poses a significant challenge for many researchers. To ensure testing at the system level with state-of-the-art AV software and simulation software, we have developed and analyzed a bridge connecting the CARLA simulator with the AV software Autoware Core/Universe. This publicly available bridge enables researchers to easily test their modules within the overall software. Our investigations show that an efficient and reliable communication system has been established. We provide the simulation bridge as open-source software at https://github.com/TUMFTM/Carla-Autoware-Bridge

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References (36)
  1. P. Tafidis, A. Pirdavani, T. Brijs, and H. Farah, “Can Automated Vehicles Improve Cyclist Safety in Urban Areas?” Safety, vol. 5, no. 3, p. 57, Aug. 2019.
  2. P. Koopman and M. Wagner, “Challenges in Autonomous Vehicle Testing and Validation,” SAE International Journal of Transportation Safety, vol. 4, no. 1, pp. 15–24, Apr. 2016.
  3. P. Koopman and M. Wagner, “Toward a Framework for Highly Automated Vehicle Safety Validation,” in WCX World Congress Experience, Apr. 2018, pp. 2018–01–1071.
  4. E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, vol. 8, pp. 58 443–58 469, 2020.
  5. S. Yang, X. Mao, S. Yang, Z. Liu, G. Chen, and S. Wang, “Towards a Robust Software Architecture for Autonomous Robot Software,” in Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering.   WCSE, 2017.
  6. M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Ng, “Ros: an open-source robot operating system,” vol. 3, 01 2009.
  7. A. Tampuu, T. Matiisen, M. Semikin, D. Fishman, and N. Muhammad, “A Survey of End-to-End Driving: Architectures and Training Methods,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1364–1384, Apr. 2022.
  8. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013. [Online]. Available: https://doi.org/10.1177/0278364913491297
  9. M. Althoff, M. Koschi, and S. Manzinger, “CommonRoad: Composable benchmarks for motion planning on roads,” in 2017 IEEE Intelligent Vehicles Symposium (IV).   Los Angeles, CA, USA: IEEE, Jun. 2017, pp. 719–726.
  10. Z. Zhong, Y. Tang, Y. Zhou, V. d. O. Neves, Y. Liu, and B. Ray, “A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation,” Dec. 2021.
  11. J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self-driving vehicles,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 9904–9913.
  12. M. Malayjerdi, G. Kaljavesi, F. Diermeyer, and R. Sell, “Scenario-based validation for autonomous vehicles with different fidelity levels,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023, pp. 3411–3416.
  13. “Autoware.” [Online]. Available: https://github.com/autowarefoundation/autoware
  14. “Apollo.” [Online]. Available: https://github.com/ApolloAuto/apollo
  15. “Autoware.” [Online]. Available: https://autoware.org/
  16. “Autoware.AI.” [Online]. Available: https://github.com/autowarefoundation/autoware ai
  17. “Apollo.” [Online]. Available: https://apollo.baidu.com/
  18. H. Shao, L. Wang, R. Chen, S. L. Waslander, H. Li, and Y. Liu, “ReasonNet: End-to-End Driving with Temporal and Global Reasoning,” May 2023, arXiv:2305.10507 [cs]. [Online]. Available: http://arxiv.org/abs/2305.10507
  19. W. Ding, C. Xu, M. Arief, H. Lin, B. Li, and D. Zhao, “A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective,” IEEE Transactions on Intelligent Transportation Systems, pp. 1–18, 2023.
  20. Z. Zhong, Y. Tang, Y. Zhou, V. d. O. Neves, Y. Liu, and B. Ray, “A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation,” Dec. 2021, arXiv:2112.00964 [cs]. [Online]. Available: http://arxiv.org/abs/2112.00964
  21. F. Rosique, P. J. Navarro, C. Fernández, and A. Padilla, “A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research,” Sensors, vol. 19, no. 3, p. 648, Jan. 2019. [Online]. Available: https://www.mdpi.com/1424-8220/19/3/648
  22. “CarMaker | IPG Automotive.” [Online]. Available: https://ipg-automotive.com/en/products-solutions/software/carmaker/
  23. G. Rong, B. H. Shin, H. Tabatabaee, Q. Lu, S. Lemke, M. Možeiko, E. Boise, G. Uhm, M. Gerow, S. Mehta, E. Agafonov, T. H. Kim, E. Sterner, K. Ushiroda, M. Reyes, D. Zelenkovsky, and S. Kim, “Lgsvl simulator: A high fidelity simulator for autonomous driving,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 2020, pp. 1–6.
  24. “esmini.” [Online]. Available: https://github.com/esmini/esmini
  25. A. Dosovitskiy, G. Ros, F. Codevilla, A. M. López, and V. Koltun, “CARLA: an open urban driving simulator,” CoRR, vol. abs/1711.03938, 2017. [Online]. Available: http://arxiv.org/abs/1711.03938
  26. “AWSIM.” [Online]. Available: https://github.com/tier4/AWSIM
  27. “Autoware.AI Simulation.” [Online]. Available: https://github.com/autowarefoundation/autoware_ai_simulation
  28. “CARLA-Apollo-Bridge.” [Online]. Available: https://github.com/guardstrikelab/carla_apollo_bridge
  29. “CARLA-ROS-Bridge.” [Online]. Available: https://github.com/carla-simulator/ros-bridge
  30. “Blender.” [Online]. Available: https://github.com/blender
  31. F. Poggenhans, J.-H. Pauls, J. Janosovits, S. Orf, M. Naumann, F. Kuhnt, and M. Mayr, “Lanelet2: A high-definition map framework for the future of automated driving,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 1672–1679.
  32. M. Dupuis, M. Strobl, and H. Grezlikowski, “Opendrive 2010 and beyond–status and future of the de facto standard for the description of road networks,” in Proc. of the Driving Simulation Conference Europe, 2010, pp. 231–242.
  33. S. Maierhofer, M. Klischat, and M. Althoff, “Commonroad scenario designer: An open-source toolbox for map conversion and scenario creation for autonomous vehicles,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, pp. 3176–3182.
  34. T. Betz, M. Schmeller, A. Korb, and J. Betz, “Latency measurement for autonomous driving software using data flow extraction,” in 2023 IEEE Intelligent Vehicles Symposium (IV), 2023, pp. 1–8.
  35. C. Bédard, I. Lütkebohle, and M. Dagenais, “ros2_tracing: Multipurpose low-overhead framework for real-time tracing of ros 2,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6511–6518, 2022.
  36. T. Betz, M. Schmeller, H. Teper, and J. Betz, “How fast is my software? latency evaluation for a ros 2 autonomous driving software,” in 2023 IEEE Intelligent Vehicles Symposium (IV) (IEEE IV 2023).   IEEE, 2023.
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