CAIAS Simulator: Self-driving Vehicle Simulator for AI Research

  • Sabir Hossain
  • Abdur R. Fayjie
  • Oualid Doukhi
  • Deok-jin LeeEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 866)


This paper presents a simulation environment which includes virtual structures of a low-cost embedded designed car for the autonomous driving test, tracks, obstacles, and environments. A cross-platform game engine, Unity 3D, empowers the embedded designed car to check and trial new tracks, parameters and calculations in the 3D environment before the real-time test. The virtual environment fabricates the domain such like that it is the mimics of the activity of a genuine car and Unity 3D are utilized to incorporate the embedded designed car into the test situation while the car’s movements and steering angle can serve as an examination premise. Distinctive driving situations were utilized to analyze how the sensors respond when they are connected to genuine circumstances and are also utilized to confirm the impacts of other parameters on the scenes. Options are available to choose flexible sensors, monitor the output and implement any autonomous driving, steering prediction, deep learning and end-to-end learning algorithm.


Simulator Autonomous vehicle AI research Sensor fusion Virtual environment 



This research was supported by Unmanned Vehicles Advanced Core Technology Research and Development Program through the National Research Foundation of Korea (NRF), Unmanned Vehicle Advanced Research Center (UVARC) funded by the Ministry of Science, ICT & Future Planning, the Republic Of Korea (No. 2016M1B3A1A01937245) and by the Ministry of Trade, Industry & Energy (MOTIE) under the R&D program (Educating Future-Car R&D Expert). (N0002428). It was also supported by Development Program through the National Research Foundation of Korea (NRF) (No. 2016R1D1A1B03935238).


  1. 1.
    Tresilian, J.: Sensorimotor control and learning: an introduction to the behavioral neuroscience of action. In: Behavioral Neuroscience. Palgrave Macmillan (1805) (2012)Google Scholar
  2. 2.
    Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: CORL (2017)Google Scholar
  3. 3.
    Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Conference on Robot Learning, California, USA (2017)Google Scholar
  4. 4.
    Tang, W., Wan, T.R.: Synthetic vision for road traffic simulation in a virtual environment. In: Intelligent Agents for Mobile and Virtual Media, pp. 176–185. Springer, London (2002)CrossRefGoogle Scholar
  5. 5.
    Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. Electron. Imaging 19, 70–76 (2017)CrossRefGoogle Scholar
  6. 6.
    Bojarski, M., Testa, D.D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P.: End to end learning for self-driving cars. In: Computer Vision and Pattern Recognition, CoRR (2016). arXiv preprint arXiv:1604.07316
  7. 7.
    Liao, H., Qu, Z.: Virtual experiment system for electrician training based on kinect and Unity 3D. In: Proceedings of the 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), pp. 2659–2662. IEEE (2013)Google Scholar
  8. 8.
    Xie, J.: Research on key technologies base Unity 3D game engine. In: 7th International Conference on Computer Science & Education (ICCSE), pp. 695–699. IEEE (2012) (July Edn.)Google Scholar
  9. 9.
    Luca, V.D., Meo, A., Mongelli, A., Vecchio, P., Paolis, L.T.D.: Development of a virtual simulator for microanastomosis: new opportunities and challenges international. In: International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 65–81. Springer, Cham (2016)Google Scholar
  10. 10.
    Rajamani, R.: Vehicle Dynamics and Control. Science & Business Media, Springer (2011)Google Scholar
  11. 11.
    Kong, J., Pfeiffer, M., Schildbach, G. and Borrelli, F.: Kinematic and dynamic vehicle models for autonomous driving control design. In: IEEE Intelligent Vehicles Symposium (IV), pp. 1094–1099. (2015) (June Edn.)Google Scholar
  12. 12.
    Wu1, J., Li1, Y., Liu1, Q., Su, G., Liu, K.: Research on application of Unity 3D in virtual battlefield environment. In: 2nd International Conference on Control, Automation, and Artificial Intelligence (CAAI), Advances in Intelligent Systems Research, vol. 134 (2017)Google Scholar
  13. 13.
    Chi, L., Mu, Y.: Deep Steering: Learning end-to-end driving model from spatial and temporal visual cues (2017). arXiv preprint arXiv:1708.03798
  14. 14.
    Wang, Y.: web article about self-driving car simulation. From: Archived from the original on July 2017. Accessed 16 Feb 2018Google Scholar
  15. 15.
    Luo, M., Claypool, M.: Uniquitous: implementation and evaluation of a cloud-based game system. In: Unity in Computer Science and Interactive Media & Game Development (GEM), Worcester, MA 01609, USA, pp. 1–6. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sabir Hossain
    • 1
  • Abdur R. Fayjie
    • 1
  • Oualid Doukhi
    • 1
  • Deok-jin Lee
    • 1
    Email author
  1. 1.Department of Mechanical and Automotive EngineeringKunsan National UniversityGunsanRepublic of Korea

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