Skip to main content

Vehicle Navigation Using Global Views

  • Chapter
Autonomous Intelligent Vehicles

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 3352 Accesses

Abstract

This chapter describes the technologies to provide a driver with the information of dynamic surroundings around the vehicle that he/she is driving to enhance his/her situation awareness. We propose capturing surroundings of a vehicle by an omnidirectional vision system mounted on the top of a vehicle and display the dynamic global view on the windshield. The mathematical model of panoramic imaging is introduced in this chapter. We then exploit panoramic inverse perspective mapping to build the mapping relationship between each image of its corresponding camera and a panoramic image. Finally, we combine the panoramic images with electronic maps which cannot only reduce driver’s (cognitive) load, but also detect and display surrounding objects in real time.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cheng, H., Liu, Z., Yang, J.: Learning feature transforms for object detection from panoramic images. In: IEEE International Conference on Multimedia and Expo, pp. 643–648 (2010)

    Google Scholar 

  2. Cheng, H., Liu, Z., Zheng, N., Yang, J.: Enhancing a driver’s situation awareness using a global view map. In: IEEE International Conference on Multimedia and Expo, pp. 1019–1022 (2007)

    Google Scholar 

  3. Cheng, H., Zheng, N., Sun, C.: Boosted gabor features applied to vehicle detection. Pattern Recognit. 1, 662–666 (2006)

    Google Scholar 

  4. Cheng, H., Zheng, N., Sun, C., van de Wetering, H.: Vanishing point and gabor feature based multi-resolution on-road vehicle detection. In: Advances in Neural Networks-ISNN, pp. 46–51 (2006)

    Google Scholar 

  5. Cutler, R., Rui, Y., Gupta, A., Cadiz, J., Tashev, I., He, L., Colburn, A., Zhang, Z., Liu, Z., Silverberg, S.: Distributed meetings: A meeting capture and broadcasting system. In: Proc. of ACM international conference on Multimedia, pp. 503–512 (2002)

    Google Scholar 

  6. Dale, R., Geldof, S., Prost, J.P.: Using natural language generation in automatic route description. J. Res. Pract. Inform. Technol. 37(1), 89 (2005)

    Google Scholar 

  7. Endsley, M.: Situation awareness global assessment technique (SAGAT). In: Proceedings of the NAECON, pp. 789–795 (1988)

    Google Scholar 

  8. Faugeras, O., Luong, Q., Papadopoulo, T.: The Geometry of Multiple Images. MIT press, Cambridge (2001)

    MATH  Google Scholar 

  9. Fleming, W.: Overview of automotive sensors. IEEE Sens. J. 1(4), 296–308 (2001)

    Article  MathSciNet  Google Scholar 

  10. Gandhi, T., Trivedi, M.M.: Dynamic panoramic surround map: motivation and omni video based approach. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, p. 61 (2005)

    Google Scholar 

  11. Gandhi, T., Trivedi, M.M.: Vehicle surround capture: Survey of techniques and a novel omni-video-based approach for dynamic panoramic surround maps. IEEE Trans. Intell. Transp. Syst. 7(3), 293–308 (2006)

    Article  Google Scholar 

  12. Gonzalez, F.G., Andres, R., Deal, D., Goergen, F., Rhodes, M., Roberts, T., Stein, G., Wilson, J., Wong, S.: Black knight: an autonomous vehicle for competition. J. Robot. Syst. 21(9), 451–460 (2004)

    Article  Google Scholar 

  13. Green, P., Levison, W., Paelke, G., Serafin, C.: Preliminary human factors design guidelines for driver information systems. NASA (19980016891) (1995)

    Google Scholar 

  14. Lee, J., Forlizzi, J., Hudson, S.: Studying the effectiveness of move: A contextually optimized in-vehicle navigation system. In: Proceedings of ACM CHI, pp. 571–580 (2005)

    Google Scholar 

  15. Stutts, J., Reinfurt, D., Staplin, L., Rodgman, E.: The Role of Driver Distraction in Traffic Crashes. AAA Foundation for Traffic Safety, Washington, DC (2001)

    Google Scholar 

  16. Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)

    Google Scholar 

  17. Szeliski, R., Shum, H.: Creating full view panoramic image mosaics and environment maps. In: Proc. of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 251–258. ACM Press, New York (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Cheng .

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag London Limited

About this chapter

Cite this chapter

Cheng, H. (2011). Vehicle Navigation Using Global Views. In: Autonomous Intelligent Vehicles. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2280-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2280-7_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2279-1

  • Online ISBN: 978-1-4471-2280-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics