The autonomous vehicle (AV) industry aims to design strategic plans to ensure the safety of the developed systems before their mass deployment. Real-road testing is shown to be impractical for validating these systems as it requires many years if not decades of testing in different environmental conditions. For solving this issue, the method should be complemented with simulation. The primary goal of this research was to develop advanced techniques in the safety validation area by using end-to-end simulation technologies. In this study, we present a simulation approach for safety evaluation of an AV shuttle, iseAuto, currently operating at the Tallinn University of Technology campus. We created a virtual environment by using geospatial data from the specified path on the university campus that includes all relevant features. Then, we converted the map to a 3D format applicable for the SVL simulator. Also, we provided the AV 3D model to use in the simulation and equipped it with the SVL virtual sensors to provide data for the Autoware perception algorithms, which is the control software of the shuttle. To show the efficiency of the proposed method, we designed two overtaking scenarios and observed the AV behaviour under the test. Finally, we demonstrate how the system enables us to evaluate AVʼs decision-making performance and safety in different situations.
1. Kalra, N. and Paddock, S. M. Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transp. Res. Part A Policy Pract., 2016, 94, 182–193.
https://doi.org/10.1016/j.tra.2016.09.010
2. Chao, Q., Jin, X., Huang, H. W., Foong, S., Yu, L. F. and Yeung, S. K. Force-based heterogeneous traffic simulation for autonomous vehicle testing. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 20–24, 2019. IEEE, 8298–8304.
https://doi.org/10.1109/ICRA.2019.8794430
3. Aeberhard, M., Rauch, S., Bahram, M., Tanzmeister, G., Thomas, J., Pilat, Y., Homm, F., Huber, W. and Kaempchen, N. Experience, results and lessons learned from automated driving on Germany’s highways. IEEE Intell. Transp. Syst. Mag., 2015, 7(1), 42–57.
https://doi.org/10.1109/MITS.2014.2360306
4. Anderson, S. J., Peters, S. C., Pilutti, T. E. and Iagnemma, K. Design and development of an optimal-control-based framework for trajectory planning threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. In Robotics Research (Pradalier, C., Siegwart, R. and Hirzinger, G., eds). Springer, Berlin, Heidelberg, 2011, 39–54.
https://doi.org/10.1007/978-3-642-19457-3_3
5. Razdan, R., Lumina, J., Balachandran, A., Cheng, C., Sreenivas, S., Fernando, X., Taiber, J., Kalia, A., Keel, N., Zuby, D., Krishnan, K., Langer, D. and Sell, R. Unsettled technology areas in autonomous vehicle test and validation. SAE Technical Paper Series, 2019.
https://doi.org/10.4271/epr2019001
6. Malayjerdi, M., Kuts, V., Sell, R., Otto, T. and Baykara, B. C. Virtual simulations environment development for autonomous vehicles interaction. In Proceedings of the ASME 2020 International Mechanical Engineering Congress and Exposition, Portland, OR, USA, November 16–19, 2020. ASME, 2B, IMECE2020-23362.
https://doi.org/10.1115/IMECE2020-23362
7. Kuts, V., Modoni, G. E., Otto, T., Sacco, M., Tähemaa, T., Bondarenko, Y. and Wang, R. Synchronizing physical factory and its digital twin through an IioT middleware: a case study. Proc. Est. Acad. Sci., 2019, 68(4), 364–370.
https://doi.org/10.3176/proc.2019.4.03
8. Kuts, V., Cherezova, N., Sarkans, M. and Otto, T. Digital Twin: industrial robot kinematic model integration to the virtual reality environment. J. Mach. Eng., 2020, 20(2), 53–64.
https://doi.org/10.36897/jme/120182
9. Lu, B., He, H., Yu, H., Wang, H., Li, G., Shi, M. and Cao, D. Hybrid path planning combining potential field with sigmoid curve for autonomous driving. Sensors, 2020, 20(24), 7197.
https://doi.org/10.3390/s20247197
10. Andersen, H., Schwarting, W., Naser, F., Eng, Y. H., Ang, M. H., Rus, D. and Alonso-Mora, J. Trajectory optimization for autonomous overtaking with visibility maxi- mization. In Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, October 16–19, 2017. IEEE, 1–8.
https://doi.org/10.1109/ITSC.2017.8317853
11. Easa, S. M. and Diachuk, M. Optimal speed plan for the overtaking of autonomous vehicles on two-lane highways. Infrastructures, 2020, 5(5), 44.
https://doi.org/10.3390/infrastructures5050044
12. Sell, R., Leier, M., Rassõlkin, A. and Ernits, J. Self-driving car ISEAUTO for research and education. In Proceedings of the 2018 19th International Conference on Research and Education in Mechatronics (REM), Delft, Netherlands, June 7–8, 2018. IEEE, 111–116.
https://doi.org/10.1109/REM.2018.8421793
13. Christophe, F., Sell, R., Bernard, A. and Coatanéa, E. Opas: Ontology processing for assisted synthesis of conceptual design solutions. In Proceedings of the ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, San Diego, USA, August 30–September 2, 2009. ASME, 49026, 249–260.
14. Sell, R., Coatanéa, E. and Christophe, F. Important aspects of early design in mechatronic. In Proceedings of the 6th International Conference of DAAAM Baltic Industrial Engineering, Tallinn, Estonia, April 24–26, 2008, 177–182.
15. Wang, R., Sell, R., Rassõlkin, A., Otto, T. and Malayjerdi, E. Intelligent functions development on autonomous electric vehicle platform. J. Mach. Eng., 2020, 20(2), 114–125.
https://doi.org/10.36897/jme/117787
16. Rassõlkin. A., Sell, R. and Leier, M. Development case study of the first estonian self-driving car, iseauto. Electr. Control Commun. Eng., 2018, 14, 81–88.
https://doi.org/10.2478/ecce-2018-0009
17. Kato, S., Tokunaga, S., Maruyama, Y., Maeda, S., Hirabayashi, M., Kitsukawa, Y., Monrroy, A., Ando, T., Fujii, Y. and Azumi, T. Autoware on board: Enabling autonomous vehicles with embedded systems. In Proceedings of the 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal, April 11–13, 2018. IEEE, 287–296.
https://doi.org/10.1109/ICCPS.2018.00035
18. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A. and Koltun, V. Carla: An open urban driving simulator. 2017, arXiv:1711.03938.
19. Rong, G., Shin, B. H., Tabatabaee, H., Lu, Q., Lemke, S., Možeiko, M., Boise, E., Uhm, G., Gerow, M., Mehta, S. et al. LGSVL simulator: A high fidelity simulator for autonomous driving. 2020, arXiv:2005.03778.
https://doi.org/10.1109/ITSC45102.2020.9294422
20. Medrano-Berumen, C., Malayjerdi, M., Akbaş, M. I., Sell, R. and Razdan, R. Development of a validation regime for an autonomous campus shuttle. In Proceedings of the 2020 SoutheastCon, Raleigh, NC, USA, March 28–29, 2020. IEEE, 1–8.
https://doi.org/10.1109/SoutheastCon44009.2020.9249692
21. Habermann, D., Hata, A., Wolf, D. and Osório, F. S. 3d point clouds segmentation for autonomous ground vehicle. In Proceedings of the 2013 III Brazilian Symposium on Computing Systems Engineering, Rio de Janeiro, Brazil, December 4–8, 2013. IEEE, 143–148.
https://doi.org/10.1109/SBESC.2013.43
22. Ding, W., Chen, B., Xu, M. and Zhao, D. Learning to collide: An adaptive safety-critical scenarios generating method. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, October 25–29, 2020. IEEE, 2243–2250.
https://doi.org/10.1109/IROS45743.2020.9340696
