Skip to main content
Springer Nature Link
Log in

Intelligent Decision-Making in Lane Detection Systems Featuring Dynamic Framework for Autonomous Vehicles

  • Conference paper
  • First Online:
Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops (SAFECOMP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14989))

Included in the following conference series:

  • 788 Accesses

Abstract

As Advanced Driver-Assistance Systems (ADAS) pave the way for autonomous vehicles, they also improve safety by decreasing the risk of hazardous events. Essential for ADAS, lane detection ensures that vehicles stay on their intended path and navigate safely. Despite their significance, the variability of lane detection algorithms can diminish performance, underscoring the need for robust and reliable solutions. We propose a novel approach to enhance decision-making in autonomous lane detection systems by introducing trust indicator metrics which help determine the usefulness of each algorithm in particular scenarios. Our dynamic framework combines a conventional algorithm with a deep learning model. This complementing combination adapts seamlessly, leveraging each other’s strengths across operational scenarios. We aim to balance the interpretability of conventional methods with the effectiveness of AI, exploring techniques like trust indicators or lane annotations. We validate the proposed framework using a self-built model vehicle demonstrator, providing insights into the real-time performance of our solution and demonstrating the potential for practical deployment.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Almotairi, K.H.: Hybrid adaptive method for lane detection of degraded road surface condition. J. King Saud Univ.-Comput. Inf. Sci. 34(8), 5261–5272 (2022)

    Google Scholar 

  2. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fus. 58, 82–115 (2020)

    Article  Google Scholar 

  3. Blazevic, R., Veledar, O., Macher, G.: Insides to trustworthy AI-based embedded systems. In: WCX SAE World Congress Experience. SAE International (2024). https://doi.org/10.4271/2024-01-2014

  4. Cantzler, H.: Random sample consensus (ransac). Institute for Perception, Action and Behaviour, Division of Informatics, University of Edinburgh 3 (1981)

    Google Scholar 

  5. Chen, W., Wang, W., Wang, K., Li, Z., Li, H., Liu, S.: Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review. J. Traffic Transp. Eng. (Eng. Ed.) 7(6), 748–774 (2020)

    Google Scholar 

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

    Google Scholar 

  7. Clement, P., Danzinger, H., Veledar, O., Könczöl, C., Macher, G., Eichberger, A.: Measuring trust in automated driving using a multi-level approach to human factors. In: 2021 24th Euromicro Conference on Digital System Design (DSD), pp. 410–417 (2021). https://doi.org/10.1109/DSD53832.2021.00069

  8. Eraqi, H.M., Moustafa, M.N., Honer, J.: End-to-end deep learning for steering autonomous vehicles considering temporal dependencies. arXiv e-prints arXiv:1710.03804 (2017)

  9. Ghanem, S., Kanungo, P., Panda, G., Parwekar, P.: An improved and low-complexity neural network model for curved lane detection of autonomous driving system. Soft. Comput. 27(1), 493–504 (2023)

    Article  Google Scholar 

  10. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89. IEEE (2018)

    Google Scholar 

  11. Huang, Y., Chen, S., Chen, Yu., Jian, Z., Zheng, N.: Spatial-temproal based lane detection using deep learning. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) AIAI 2018. IAICT, vol. 519, pp. 143–154. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92007-8_13

    Chapter  Google Scholar 

  12. Illingworth, J., Kittler, J.: A survey of the hough transform. Comput. Vis. Graphics Image Process. 44(1), 87–116 (1988)

    Article  Google Scholar 

  13. Kim, J., Kim, J., Jang, G.J., Lee, M.: Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Netw. 87, 109–121 (2017)

    Article  Google Scholar 

  14. Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Maasberg, M., Choo, K.K.R.: Multimedia big data computing and internet of things applications: a taxonomy and process model. J. Netw. Comput. Appl. 124, 169–195 (2018)

    Article  Google Scholar 

  15. Li, Z.Q., Ma, H.M., Liu, Z.Y.: Road lane detection with gabor filters. In: 2016 International Conference on Information System and Artificial Intelligence (ISAI), pp. 436–440 (2016). https://doi.org/10.1109/ISAI.2016.0099

  16. Macher, G., et al.: Dependable integration concepts for human-centric AI-based systems. In: Habli, I., Sujan, M., Gerasimou, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2021. LNCS, vol. 12853, pp. 11–23. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-83906-2_1

    Chapter  Google Scholar 

  17. Noh, S.: Decision-making framework for autonomous driving at road intersections: safeguarding against collision, overly conservative behavior, and violation vehicles. IEEE Trans. Industr. Electron. 66(4), 3275–3286 (2018)

    Article  Google Scholar 

  18. Pareigis, S., Maaß, F.L.: Improved robust neural network for sim2real gap in system dynamics for end-to-end autonomous driving. In: Gini, G., Nijmeijer, H., Burgard, W., Filev, D. (eds.) Informatics in Control, Automation and Robotics, pp. 1–21. Springer, Cham (2023). https://doi.org/10.1007/978-3-030-83906-2_1

    Chapter  Google Scholar 

  19. Tang, J., Li, S., Liu, P.: A review of lane detection methods based on deep learning. Pattern Recogn. 111, 107623 (2021)

    Article  Google Scholar 

  20. Ullah, A., Muhammad, K., Haq, I.U., Baik, S.W.: Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments. Futur. Gener. Comput. Syst. 96, 386–397 (2019)

    Article  Google Scholar 

  21. Vajak, D., Vranješ, M., Grbić, R., Teslić, N.: A rethinking of real-time computer vision-based lane detection. In: 2021 IEEE 11th International Conference on Consumer Electronics (ICCE-Berlin), pp. 1–6. IEEE (2021)

    Google Scholar 

  22. Yang, W., Zhang, X., Lei, Q., Shen, D., Xiao, P., Huang, Y.: Lane position detection based on long short-term memory (LSTM). Sensors 20(11), 3115 (2020)

    Article  Google Scholar 

  23. Yusuf, M.M., Karim, T., Saif, A.S.: A robust method for lane detection under adverse weather and illumination conditions using convolutional neural network. In: Proceedings of the International Conference on Computing Advancements, pp. 1–8 (2020)

    Google Scholar 

  24. Zhang, S., Yang, L., Mi, M.B., Zheng, X., Yao, A.: improving deep regression with ordinal entropy. arXiv e-prints arXiv:2301.08915 (2023). https://doi.org/10.48550/arXiv.2301.08915

Download references

Author information

Authors and Affiliations

  1. Institute of Technical Informatics, Graz University of Technology, Graz, Austria

    Romana Blazevic, Fynn Luca Maaß, Omar Veledar & Georg Macher

Authors
  1. Romana Blazevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fynn Luca Maaß
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar Veledar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Georg Macher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Romana Blazevic or Georg Macher .

Editor information

Editors and Affiliations

  1. University of Florence, Florence, Italy

    Andrea Ceccarelli

  2. Fraunhofer IKS, Munich, Germany

    Mario Trapp

  3. University of Florence, Florence, Italy

    Andrea Bondavalli

  4. AIT Austrian Institute of Technology GmbH, Vienna, Austria

    Erwin Schoitsch

  5. Mälardalens University, Vasteras, Sweden

    Barbara Gallina

  6. GTS Deutschland GmbH, Ditzingen, Germany

    Friedemann Bitsch

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Blazevic, R., Maaß, F.L., Veledar, O., Macher, G. (2024). Intelligent Decision-Making in Lane Detection Systems Featuring Dynamic Framework for Autonomous Vehicles. In: Ceccarelli, A., Trapp, M., Bondavalli, A., Schoitsch, E., Gallina, B., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. SAFECOMP 2024. Lecture Notes in Computer Science, vol 14989. Springer, Cham. https://doi.org/10.1007/978-3-031-68738-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-68738-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-68737-2

  • Online ISBN: 978-3-031-68738-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics