Skip to main content

Autonomous Vehicles

  • Chapter
  • First Online:
Intelligent Transport System in Smart Cities

Abstract

Autonomous vehicles incorporate a large diversity of cutting-edge technologies to enable self-driving capabilities and enhance the experience of drivers and passengers. In general, systems based on these technologies rely on increasingly sophisticated sensors and actuators, which allow vehicles to detect events in the environment, providing their embedded systems with means to infer and make route and navigation decisions and supporting vehicles with greater driving autonomy. Full implementation of self-driving vehicles still involves several concerns related to the implications and threats that minor faults in the system may pose to safety; these faults could cause accidents and place human lives at high risk. Therefore, the design of a standalone vehicle demands significant attention to the safety and reliability of the driving system. These concerns have led the automotive industry to invest heavily in embedded electronic systems to achieve comprehensive safety, comfort, stability, and performance.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Alix R, Le Coat F, Aubert D (2003) Flat world homography for non-flat world on-road obstacle detection. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 310–315

    Google Scholar 

  2. Barnes N, Zelinsky A (2004) Real-time radial symmetry for speed sign detection. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 566–571

    Google Scholar 

  3. Benenson R (2008) Perception pour véhicule urbain sans conducteur: conception et implementation. PhD thesis, Tese (Doutorado)-École des Mines de Paris, Paris Tech, Paris

    Google Scholar 

  4. Bertozzi M, Broggi A, Cellario M, Fascioli A, Lombardi P, Porta M (2002) Artificial vision in road vehicles. Proc IEEE 90(7):1258–1271

    Article  Google Scholar 

  5. Broggi A, Zelinsky A, Özgüner Ü, Laugier C (2016) Intelligent vehicles. Springer, Cham, pp 1627–1656

    Google Scholar 

  6. Chuanjin L, Xiaohu Q, Xiyue H, Yi C, Xin Z (2003) A monocular-vision-based driver assistance system for collision avoidance. In: Proceedings of the IEEE intelligent transportation systems, vol 1. IEEE, Piscataway, pp 463–468

    Google Scholar 

  7. Crowley J, Martin J (1995) Experimental comparison of correlation techniques. In: Proceedings of the international conference on intelligent autonomous systems

    Google Scholar 

  8. Demonceaux C, Kachi-Akkouche D (2004) Robust obstacle detection with monocular vision based on motion analysis. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 527–532

    Google Scholar 

  9. dos Santos CT, Osório FS (2004) An intelligent and adaptive virtual environment and its application in distance learning. In: Proceedings of the working conference on advanced visual interfaces. ACM, New York, pp 362–365

    Google Scholar 

  10. Dudek G, Jenkin M (2010) Computational principles of mobile robotics. Cambridge University Press, Cambridge

    Book  Google Scholar 

  11. Ziegler J, Lategahn H, Schreiber M, Keller CG, Knöppel C, Hipp J, Haueis M, Stiller C (2014) Video based localization for Bertha. In: Proceedings of the IEEE intelligent vehicles symposium proceedings. IEEE, Dearborn, MI, pp 1231–1238

    Google Scholar 

  12. Fang CY, Chen SW, Fuh CS (2003) Road-sign detection and tracking. IEEE Trans Veh Technol 52(5):1329–1341

    Article  Google Scholar 

  13. Fardi B, Scheunert U, Cramer H, Wanielik G (2003) A new approach for lane departure identification. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 100–105

    Google Scholar 

  14. Gavrila DM, Giebel J, Munder S (2004) Vision-based pedestrian detection: the protector system. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 13–18

    Google Scholar 

  15. Heinen FJ, Osório FS (2002) Hycar-a robust hybrid control architecture for autonomous robots. In: Proceedings of the HIS, pp 830–842

    Google Scholar 

  16. Jung CR, Kelber CR (2004) A lane departure warning system based on a linear-parabolic lane model. In: Proceedings of the intelligent vehicles symposium. IEEE, Piscataway, pp 891–895

    Google Scholar 

  17. Jung CR, Kelber CR (2004) A robust linear-parabolic model for lane following. In: Proceedings of the Brazilian symposium on computer graphics and image processing. IEEE, Piscataway, pp 72–79

    Chapter  Google Scholar 

  18. Kastrinaki V, Zervakis M, Kalaitzakis K (2003) A survey of video processing techniques for traffic applications. Image Vis Comput 21(4):359–381

    Article  Google Scholar 

  19. Kelber CR, Dreger RS, Schirmbeck J, Borges DA (2002) Nonlinear steering control strategy for an optical stripe tracker. In: Proceedings of the 7th international workshop on advanced motion control. IEEE, Piscataway, pp 546–550

    Google Scholar 

  20. Kelber CR, Webber W, Gomes GK, Lohmann MA, Rodrigues MS, Ledur D (2004) Active steering unit with integrated ACC for x-by-wire vehicles using a joystick as HMI. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 173–177

    Google Scholar 

  21. Klette R (2015) Vision-based driver assistance. In: Wiley encyclopedia of electrical and electronics engineering. Wiley, Hoboken

    Book  Google Scholar 

  22. Kluge K (1994) Extracting road curvature and orientation from image edge points without perceptual grouping into features. In: Proceedings of the intelligent vehicles symposium. IEEE, Piscataway, pp 109–114

    Chapter  Google Scholar 

  23. Latombe JC (2012) Robot motion planning, vol 124. Springer, Berlin

    Google Scholar 

  24. Lee JW (2002) A machine vision system for lane-departure detection. Comput Vis Image Underst 86(1):52–78

    Article  Google Scholar 

  25. Linfeng L, Hai W, Ping H, Huifang K, Ming Y, Canghua J, Zhihong M (2017) Robust chattering-free sliding mode control of electronic throttle systems in drive-by-wire vehicles. In: Proceedings of the 36th Chinese control conference, pp 9513–9518

    Google Scholar 

  26. Matsumoto Y, Ikeda K, Inaba M, Inoue H (1999) Visual navigation using omnidirectional view sequence. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems. IEEE, Piscataway, vol 1, pp 317–322

    Google Scholar 

  27. Medeiros AA (1998) A survey of control architectures for autonomous mobile robots. J Braz Comput Soc 4(3). http://dx.doi.org/10.1590/S0104-65001998000100004

  28. Meneguette RI, Boukerche A, Pimenta AHM, Meneguette M (2017) A resource allocation scheme based on Semi-Markov Decision Process for dynamic vehicular clouds. In: Proceedings of the IEEE international conference on communications, pp 1–6

    Google Scholar 

  29. Ozguner U, Stiller C, Redmill K (2007) Systems for safety and autonomous behavior in cars: the DARPA grand challenge experience. Proc IEEE 95(2):397–412

    Article  Google Scholar 

  30. Paden B, Cap M, Yong SZ, Yershov D, Frazzoli E (2016) A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans Intell Veh 1(1):33–55

    Article  Google Scholar 

  31. Risack R, Mohler N, Enkelmann W (2000) A video-based lane keeping assistant. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 356–361

    Google Scholar 

  32. Stein GP, Mano O, Shashua A (2000) A robust method for computing vehicle ego-motion. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 362–368

    Google Scholar 

  33. Thorpe C, Hebert MH, Kanade T, Shafer SA (1988) Vision and navigation for the Carnegie-Mellon Navlab. IEEE Trans Pattern Anal Mach Intell 10(3):362–373

    Article  Google Scholar 

  34. Vis IF (2006) Survey of research in the design and control of automated guided vehicle systems. Eur J Oper Res 170(3):677–709

    Article  MathSciNet  Google Scholar 

  35. Wang Y, Shen D, Teoh EK (2000) Lane detection using spline model. Pattern Recogn Lett 21(8):677–689

    Article  Google Scholar 

  36. Weiss K, Kaempchen N, Kirchner A (2004) Multiple-model tracking for the detection of lane change maneuvers. In: Proceedings of the IEEE intelligent vehicles symposium. IEEE, Piscataway, pp 937–942

    Google Scholar 

  37. Yue Wang EKT, Shen D (2004) Lane detection and tracking using b-snake, image and vision computer. Image Vis Comput 22:269–280

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

I. Meneguette, R., E. De Grande, R., A. F. Loureiro, A. (2018). Autonomous Vehicles. In: Intelligent Transport System in Smart Cities. Urban Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-93332-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93332-0_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93331-3

  • Online ISBN: 978-3-319-93332-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics