Skip to main content
SpringerLink
Log in

Explainable navigation system using fuzzy reinforcement learning

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

Explainable outcomes in autonomous navigation have become crucial for drivers, other vehicles, as well as for pedestrians. Creating trustworthy strategies is mandatory for the integration of self-driving cars into quotidian environments. This paper presents the successful implementation of an explainable Fuzzy Deep Reinforcement Learning approach for autonomous vehicles based on the AWS DeepRacer\(^{\mathrm{TM}}\) platform. A model of the environment is created by transforming crisp values into linguistic variables. A fuzzy inference system is used to define the reward of the vehicle depending on its current state. Guidelines to define the actions and to improve performance of the reinforcement learning agent are given based on the characteristics of the existing hardware. The performance of the models is tested on tracks with distinctive properties using agents with different policies and action spaces, and shows explainable and successful navigation of the agent on diverse scenarios.

Graphic Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Abbreviations

AWS:

Amazon Web Services

AI:

Artificial intelligence

XAI:

Explainable artificial intelligence

FIS:

Fuzzy inference system

MF:

Membership function

ML:

Machine learning

DL:

Deep learning

DNN:

Deep neural network

DNN:

Convolutional neural network

RL:

Reinforcement learning

References

  1. Montemerlo, M., Thrun, S., Dahlkamp, H., Stavens, D., Strohband, S.: Winning the DARPA grand challenge with an AI robot. In: Proceedings of the AAAI National Conference on Artificial Intelligence, Boston, MA. AAAI (2006)

  2. SAE International J3016. https://www.sae.org/standards/content/j3016_201401/. Accessed 12 Apr 2020

  3. Urmson, C., et al.: Tartan Racing: A Multi-Modal Approach to the DARPA Urban Challenge. DARPA, Arlington County (2007)

    Google Scholar 

  4. Veres, S., Molnar, L., Lincoln, N., Morice, P.: Autonomous vehicle control systems: a review of decision making. Proc. Inst. Mech. Eng. I J. Syst. Control Eng. 225(2), 155–195 (2011)

    Google Scholar 

  5. Althoff, M., Lösch, R.: Can automated road vehicles harmonize with traffic flow while guaranteeing a safe distance?. In: Proceedings of IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 485–491 (2016)

  6. Vagg, C., Brace, C.J., Hari, D., Akehurst, S., Poxon, J., Ash, L.: Development and field trial of a driver assistance system to encourage eco-driving in light commercial vehicle fleets. IEEE Trans. Intell. Transp. Syst. 14(2), 796–805 (2013)

    Article  Google Scholar 

  7. Andersen, H., et al.: Trajectory optimization for autonomous overtaking with visibility maximization. In: Proceedings of IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 1–8 (2017)

  8. Zhang, K., Yang, A., Su, H., de La Fortelle, A., Miao, K., Yao, Y.: Service-oriented cooperation models and mechanisms for heterogeneous driverless vehicles at continuous static critical sections. IEEE Trans. Intell. Transp. Syst. 18(7), 1867–1881 (2016)

    Article  Google Scholar 

  9. Menéndez-Romero, C., Sezer, M., Winkler, F., Dornhege, C., Burgard, W.: Courtesy behavior for highly automated vehicles on highway interchanges. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 943–948 (2018)

  10. Li, L., Wen, D., Yao, Y.: A survey of traffic control with vehicular communications. IEEE Trans. Intell. Transp. Syst 15(1), 425–432 (2014)

    Article  MathSciNet  Google Scholar 

  11. Morignot, P., Rastelli, J.P., Nashashibi, F.: Arbitration for balancing control between the driver and ADAS systems in an automated vehicle: survey and approach. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 575–580 (2014)

  12. Broggi, A., Debattisti, S., Panciroli, M., Porta, P.: Moving from analog to digital driving. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 1113–1118 (2013)

  13. Althoff, M., Koschi, M., Manzinger, S. CommonRoad: composable benchmarks for motion planning on roads. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 719–726 (2017)

  14. Morignot, P., Nashashibi, F.: An ontology-based approach to relax traffic regulation for autonomous vehicle assistance. In: Proceedings of IASTED International Conference on Artificial Intelligence and Applications, pp. 10–17 (2013)

  15. Du, N., Zhou, F., Pulver, E., Tilbury, D., Robert, L., Pradhan, A., Yang, X.: Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving. Transp. Res. C Emerg. Technol. 112, 78–87 (2020)

    Article  Google Scholar 

  16. Jayaraman, S., Chandler, C., Tilbury, D., Yang, X., Pradhan, A., Tsui, K., Robert, L.: Pedestrian trust in automated vehicles: role of traffic signal and AV driving behavior. Front. Robot. AI 6(117) (2019)

  17. Vasiljević, G., Miklić, D., Draganjac, I., Kovačić, Z., Lista, P.: High-accuracy vehicle localization for autonomous warehousing. Robot. Comput. Integr. Manuf. 42, 1–16 (2016)

    Article  Google Scholar 

  18. Schneemann, F., Gohl, I.: Analyzing driver-pedestrian interaction at crosswalks: a contribution to autonomous driving in urban environments. In: IEEE Intelligent Vehicles Symposium (IV), Gothenburg, pp. 38–43 (2016)

  19. Claussmann, L., Revilloud, M., Glaser, S., Gruyer, D.: A study on al-based approaches for high-level decision making in highway autonomous driving. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3671–3676 (2017)

  20. Claussmann, L., Revilloud, M., Gruyer, D., Glaser, S.: A review of motion planning for highway autonomous driving. IEEE Trans. Intell. Transp. Syst. 21(5), 1826–1848 (2019)

    Article  Google Scholar 

  21. Balal, E., Cheu, R.L., Sarkodie-Gyan, T.: A binary decision model for discretionary lane changing move based on fuzzy inference system. Transp. Res. C Emerg. Technol. 67, 47–61 (2016)

    Article  Google Scholar 

  22. Lefévre, S., Carvalho, A., Borrelli, F.: A learning-based framework for velocity control in autonomous driving. IEEE Trans. Autom. Sci. Eng. 13(1), 32–42 (2016)

    Article  Google Scholar 

  23. Li, N., Oyler, D.W., Zhang, M., Yildiz, Y., Kolmanovsky, I., Girard, R.: Game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems. IEEE Trans. Control Syst. Technol. 26(5), 1782–1797 (2018)

    Article  Google Scholar 

  24. Huy, Q., Mita, S., Nejad, H.T.N., Han, L.: Dynamic and safe path planning based on support vector machine among multi moving obstacles for autonomous vehicles. IEICE Trans. Inf. Syst. E96–D(2), 314–328 (2013)

    Google Scholar 

  25. Lefévre, S., Vasquez, D., Laugier, C.: A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J. 1(1), 1–14 (2014)

    Article  Google Scholar 

  26. Constantin, A. Park, J., Iagnemma, K.: A margin-based approach to threat assessment for autonomous highway navigation. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp. 234–239 (2014)

  27. Ardelt, M., Waldmann, P., Homm, F., Kaempchen, N.: Strategic decision-making process in advanced driver assistance systems. IFAC Proc. Vol. 43(7), 566–571 (2010)

    Article  Google Scholar 

  28. Chen, C., Seff, A., Kornhauser, A., Xiao, J.: DeepDriving: learning affordance for direct perception in autonomous driving. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2722–2730 (2015)

  29. Yang, L., Liang, X., Wang, T., Xing, E.: Real-to-virtual domain unification for end-to-end autonomous driving. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 530–545 (2018)

  30. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., Zieba, K.: End to End Learning for Self-Driving Cars (2016)

  31. Chen, W., Qu, T., Zhou, Y., Weng, K., Wang, G., Fu, G.: Door recognition and deep learning algorithm for visual based robot navigation. In: IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), Bali, pp. 1793–1798 (2014)

  32. Zhu, Y., et al.: Target-driven visual navigation in indoor scenes using deep reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, pp. 3357–3364 (2017)

  33. Richter, C., Nicholas, R.: Safe Visual Navigation Via Deep Learning and Novelty Detection. Robotics: Science and Systems XIII (2017). https://doi.org/10.15607/RSS.2017.XIII.064

  34. Kaufmann, E., Loquercio, A., Ranftl, R., Dosovitskiy, A., Koltun, V., Scaramuzza, D.: Deep Drone Racing: Learning Agile Flight in Dynamic Environments, Conference on Robot Learning. (CORL), Zurich (2018)

    Google Scholar 

  35. Jung, S., Hwang, S., Shin, H., Shim, D.H.: Perception, guidance, and navigation for indoor autonomous drone racing using deep learning. IEEE Robot. Autom. Lett. 3(3), 2539–2544 (2018)

    Article  Google Scholar 

  36. Shou, Z., Di, X.: Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning Sci Dir. 119, (2020). https://doi.org/10.1016/j.trc.2020.102738

  37. Samek, W., Müller, K.: Towards Explainable Artificial Intelligence, Explainable AI: Interpreting. Explaining and Visualizing Deep Learning. Springer, Berlin (2019)

    Book  Google Scholar 

  38. Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Advances in Neural Information Processing Systems (NIPS). pp. 1135–1143 (2015)

  39. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks (2018)

  40. Samek, W., Wiegand, T., Müller, K.R.: Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. ITU J. ICT Discov. 1(1), 39–48 (2018)

    Google Scholar 

  41. Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., Sayres, R.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: International Conference on Machine Learning (ICML), pp. 2673–2682 (2018)

  42. Montavon, G., Samek, W., Müller, K.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1–15 (2018)

    Article  MathSciNet  Google Scholar 

  43. Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., Müller, K.: Unmasking clever hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019)

    Article  Google Scholar 

  44. Koh, P., Liang, P.: Understanding black-box predictions via influence functions. In: International Conference on Machine Learning (ICML), pp. 1885–1894 (2017)

  45. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  46. Haspiel, J., Du, N., Meyerson, J., Robert, L., Tilbury, D., Yang, X., Pradhan, A.: Explanations and Expectations: Trust Building in Automated Vehicles, pp. 119–120 (2018)

  47. Holzinger, A., Biemann, C., Pattichis, C., Kell, D.: What do we need to build explainable AI systems for the medical domain? Explainable AI for the Medical Domain (2017)

  48. Amarasinghe, K., Kenney, K., Manic, M.: Toward explainable deep neural network based anomaly detection. In: 11th International Conference on Human System Interaction (HSI), Gdansk, pp. 311–317 (2018)

  49. Fernandez, A., Herrera, F., Cordon, O., Jose del Jesus, M., Marcelloni, F.: Evolutionary fuzzy systems for explainable artificial intelligence: why, when, what for, and where to? IEEE Comput. Intell. Mag. 14(1), 69–81 (2019)

    Article  Google Scholar 

  50. Fürnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, New York (2012)

    Book  Google Scholar 

  51. Kuncheva, L.: How good are fuzzy if-then classifiers? IEEE Trans. Syst. Man Cybern. B 30(4), 501–509 (2000)

    Article  Google Scholar 

  52. Mencar, C., Alonso, J.: Paving the way to explainable artificial intelligence with fuzzy modelling. In: Fuzzy Logic and Applications: 12th International Workshop, pp. 215–226 (2018)

  53. Morales-Vargas, E., Reyes-García, C., Peregrina-Barreto, H., Orihuela-Espina, F.: Facial expression recognition with fuzzy explainable models. In: Models and Analysis of Vocal Emissions for Biomedical Applications: 10th International Workshop (2017)

  54. Keneni, B., et al.: Evolving rule-based explainable artificial intelligence for unmanned aerial vehicles. IEEE Access 7, 17001–17016 (2019)

    Article  Google Scholar 

  55. Deng, Y., Ren, Z., Kong, Y., Bao, F., Dai, Q.: A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans. Fuzzy Syst. 25(4), 1006–1012 (2017)

    Article  Google Scholar 

  56. Lee, C., Teng, C.: Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans. Fuzzy Syst. 8(4), 349–366 (2000)

    Article  Google Scholar 

  57. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  58. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Signal Process. 7, 3–4 (2013)

    MathSciNet  MATH  Google Scholar 

  59. Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd Edition (2010)

  60. Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  61. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 1, 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  62. Ponce-Cruz, P., Ramírez-Figueroa, F.: Intelligent Control Systems with LabVIEW. Springer, Berlin (2010)

    Book  Google Scholar 

  63. Ponce-Cruz, P.: Inteligencia Artificial con Aplicaciones a la Ingeniería, Editorial Alfaomega (2011)

  64. Knapp, R., Agarwal, U., Djamschidi, R., Layeghi, S., Dastamalchi, M.: The use of fuzzy set classification for pattern recognition of the polygraph. In: IEEE 3rd International Fuzzy Systems Conference (1995)

  65. Driankov, D., Saffiotti, A.: Fuzzy Logic in Autonomous Navigation. Springer, Berlin (2001)

    Book  Google Scholar 

  66. Wu, D.: Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. In: IEEE International Conference on Fuzzy Systems (2012)

  67. AWS DeepRacer Developer Guide: Amazon Web Services Inc. (2020). https://docs.aws.amazon.com/deepracer/latest/ developerguide/awsracerdg.pdf. Accessed 12 Apr 2020

Download references

Acknowledgements

The authors would like to thank Nora Clancy Kelsall for her English Language editing, and Dr. David Balderas-Silva and Dr. Renato Galluzzi review services. This research is being supported by the Laboratory of Computer Intelligente, Mechatronics and Biodesign (CIMB) at Tecnologico de Monterrey.

Funding

Funding was provided by Tecnologico de Monterrey - Grant No. A00996397, and Consejo Nacional de Ciencia y Tecnologia (CONACYT) by the scholarship 679120.

The authors would like to acknowledge the financial support of the Novus Grant with PEP no. PHHT032-19ZZ00013, TecLabs, Tecnologico de Monterrey, in the production of this work.

Author information

Authors and Affiliations

  1. Tecnologico de Monterrey, Campus Ciudad de Mexico, Calle del Puente 222, Col. Ejidos de Huipulco, Tlalpan, CDMX, Mexico

    Rolando Bautista-Montesano, Rogelio Bustamante-Bello & Ricardo A. Ramirez-Mendoza

Authors
  1. Rolando Bautista-Montesano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rogelio Bustamante-Bello
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo A. Ramirez-Mendoza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rolando Bautista-Montesano or Ricardo A. Ramirez-Mendoza.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Code availability

All the versions of the Deep Reinforcement Learning Fuzzy Inference System are located in this Github repository https://github.com/Rolix57/RL-FISRolix57/RL-FIS.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bautista-Montesano, R., Bustamante-Bello, R. & Ramirez-Mendoza, R.A. Explainable navigation system using fuzzy reinforcement learning. Int J Interact Des Manuf 14, 1411–1428 (2020). https://doi.org/10.1007/s12008-020-00717-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-020-00717-1

Keywords

Search

Navigation