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Autonomous Intelligent Vehicles pp 3–11Cite as

Introduction

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

Abstract

In this chapter, autonomous intelligent vehicles considered as mobile robot platforms are introduced. In general, an intelligent vehicle consists of four fundamental technologies: environment perception and modeling, vehicle localization and map building, path planning and decision-making, and motion control. On the one hand, these platforms, combining vehicles, drivers and lanes together, are designed to improve road safety and reduce traffic congestion and accidents in intelligent transportation systems. On the other hand, the Defense Advanced Research Project Agency (DARPA) had held the Grand Challenges and the Urban Challenges, which remarkably promoted the technologies of entirely autonomous intelligent vehicles and their military applications around the world.

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Notes

  1. 1.

    http://marsrovers.jpl.nasa.gov/overview/.

  2. 2.

    http://www.urban-challenge.com/_eng/.

  3. 3.

    http://ccvai.xjtu.edu.cn.

References

  1. National Research Council (US) Board on Army Science, National Research Council (US) Commission on Engineering, Technical Systems: Star 21: strategic technologies for the army of the twenty-first century. National Academies (1993)

    Google Scholar 

  2. Betke, M., Haritaoglu, E., Davis, L.S.: Real-time multiple vehicle detection and tracking from a moving vehicle. Mach. Vis. Appl., 12(2), 69–83 (2000)

    Article  Google Scholar 

  3. Broggi, A.: Automatic Vehicle Guidance: the Experience of the ARGO Autonomous Vehicle. World Scientific, Singapore (1999)

    Book  MATH  Google Scholar 

  4. Brooks, R., Iyengar, S.: Multi-sensor Fusion: Fundamentals and Applications with Software. Prentice-Hall, New York (1998)

    Google Scholar 

  5. Buehler, M., Iagnemma, K., Singh, S.: Special issue on the 2007 DARPA Urban Challenge, Part II. J. Field Robot., 25(9), 567–724 (2008)

    Article  Google Scholar 

  6. Carsten, J., Rankin, A., Ferguson, D., Stentz, A.: Global path planning on board the Mars exploration rovers. In: Proc. of the IEEE Aerospace Conference (2007)

    Google Scholar 

  7. Cheng, H., Zheng, N., Qin, J.: Pedestrian detection using sparse gabor filter and support vector machine. In: Proc. of the IEEE Intelligent Vehicles Symposium (2005)

    Google Scholar 

  8. Csorba, M.: Simultaneous localisation and map building. Robotic Recherche Group Department of Engineering of Oxford

    Google Scholar 

  9. Dagan, E., Mano, O., Stein, G.P., Shashua, A.: Forward collision warning with a single camera. In: IEEE Intelligent Vehicles Symposium (2004)

    Google Scholar 

  10. Fletcher, L., Zelinsky, A.: Context sensitive driver assistance based on gaze–road scene correlation. In: Experimental Robotics. Springer, Berlin (2008)

    Google Scholar 

  11. Giesbrecht, J.: Global path planning for unmanned ground vehicles. Technical report, Defense Research and Development Suffield (Alberta) (2004)

    Google Scholar 

  12. Godbole, D.N., Lygeros, J.: Longitudinal control of the lead car of a platoon. IEEE Trans. Veh. Technol., 43(4), 1125–1135 (1994)

    Article  MathSciNet  Google Scholar 

  13. Hayati, S., Volpe, R., Backes, P., Balaram, J., Welch, R., Ivlev, R., Tharp, G., Peters, S., Ohm, T., Petras, R., et al.: The Rocky 7 Rover: a Mars science craft prototype. In: Proc. of the IEEE International Conference on Robotics and Automation, vol. 3, pp. 2458–2464 (1997)

    Chapter  Google Scholar 

  14. Herpel, T., Lauer, C., German, R., Salzberger, J.: Multi-sensor data fusion in automotive applications. In: IEEE International Conference on Sensing Technology (2008)

    Google Scholar 

  15. Hoc, J., Mars, F., Milleville-Pennel, I., Jolly, E., Netto, M., Blosseville, J.: Evaluation of human-machine cooperation modes in car driving for safe lateral control in bends: function delegation and mutual control modes. Le Travail Humain, 69, 115–185 (2006)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  17. Leibe, B., Cornelis, N., Cornelis, K., Van Gool, L.: Dynamic 3D scene analysis from a moving vehicle. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  18. Leonard, J.J., Durrant-Whyte, H.F.: Directed Sonar Sensing for Mobile Robot Navigation. Springer, Berlin (1992)

    Book  MATH  Google Scholar 

  19. Li, L., Wang, F.Y.: Advanced Motion Control and Sensing for Intelligent Vehicles. Springer, Berlin (2007)

    MATH  Google Scholar 

  20. Moravec, H.P.: Sensor fusion in certainty grids for mobile robots. AI Mag., 9(2), 61 (1988)

    Google Scholar 

  21. O’Brien, R., Iglesias, P., Urban, T.: Vehicle lateral control for automated highway systems. IEEE Trans. Control Syst. Technol., 4(3), 266–273 (1996)

    Article  Google Scholar 

  22. Pagac, D., Nebot, E.M., Durrant-Whyte, H.: An evidential approach to map-building for autonomous vehicles. IEEE Trans. Robot. Autom. 14(4), 623–629 (1998)

    Article  Google Scholar 

  23. Paris, A., Adonis, C.: Exploring Mars using intelligent robots. Technical report (1995)

    Google Scholar 

  24. Pham, H., Hedrick, K., Tomizuka, M.: Combined lateral and longitudinal control of vehicles for IVHS. In: American Control Conference. IEEE, New York (1994).

    Google Scholar 

  25. Rajamani, R., Zhu, C.: Semi-autonomous adaptive cruise control systems. IEEE Trans. Veh. Technol., 51(5), 1186–1192 (2002)

    Article  Google Scholar 

  26. Siegwart, R., Nourbakhsh, I.R.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge (2004)

    Google Scholar 

  27. Srinivasa, N.: Vision-based vehicle detection and tracking method for forward collision warning in automobiles. In: IEEE Intelligent Vehicle Symposium (2002)

    Google Scholar 

  28. Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (1999)

    Google Scholar 

  29. Swaroop, D., Hedrick, J.K., Choi, S.: Direct adaptive longitudinal control of vehicle platoons. IEEE Trans. Veh. Technol., 50(1), 150–161 (2001)

    Article  Google Scholar 

  30. Urmson, C., Anhalt, J., Clark, M., Galatali, T., Gonzalez, J.P., Gowdy, J., Gutierrez, A., Harbaugh, S., Johnson-Roberson, M., Kato, H., et al.: High speed navigation of unrehearsed terrain: Red team technology for grand challenge 2004. CMU-RI Tech. Rep. (2004)

    Google Scholar 

  31. Urmson, C., Ragusa, C., Ray, D., Anhalt, J., Bartz, D., Galatali, T., Gutierrez, A., Johnston, J., Harbaugh, S., et al.: A robust approach to high-speed navigation for unrehearsed desert terrain. J. Field Robot., 23(8), 467–508 (2006)

    Article  MATH  Google Scholar 

  32. Vahidi, A., Eskandarian, A.: Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans. Intell. Transp. Syst., 4(3), 143–153 (2003)

    Article  Google Scholar 

  33. Van Arem, B., Van Driel, C.J.G., Visser, R.: The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans. Intell. Transp. Syst., 7(4), 429–436 (2006)

    Article  Google Scholar 

  34. Weilkes, M., Burkle, L., Rentschler, T., Scherl, M.: Future vehicle guidance assistance-combined longitudinal and lateral control. Automatisierungstechnik, 53(1), 4–10 (2005)

    Article  Google Scholar 

  35. Wu, H., Siegel, M., Stiefelhagen, R., Yang, J.: Sensor fusion using Dempster–Shafer theory. In: IEEE Instrumentation and Measurement Technology Conference Proceedings (2002)

    Google Scholar 

  36. Zheng, N., Tang, S., Cheng, H., Li, Q., Lai, G., Wang, F.W.: Toward intelligent driver-assistance and safety warning system. IEEE Intell. Syst., 19(2), 8–11 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology, 610054, Chengdu, Sichuan, People’s Republic of China

    Prof. Hong Cheng

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  1. Prof. Hong Cheng
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Correspondence to Hong Cheng .

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© 2011 Springer-Verlag London Limited

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Cheng, H. (2011). Introduction. In: Autonomous Intelligent Vehicles. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2280-7_1

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  • DOI: https://doi.org/10.1007/978-1-4471-2280-7_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2279-1

  • Online ISBN: 978-1-4471-2280-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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