Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

Model based vehicle detection and tracking for autonomous urban driving

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

Situational awareness is crucial for autonomous driving in urban environments. This paper describes the moving vehicle detection and tracking module that we developed for our autonomous driving robot Junior. The robot won second place in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007. The module provides reliable detection and tracking of moving vehicles from a high-speed moving platform using laser range finders. Our approach models both dynamic and geometric properties of the tracked vehicles and estimates them using a single Bayes filter per vehicle. We present the notion of motion evidence, which allows us to overcome the low signal-to-noise ratio that arises during rapid detection of moving vehicles in noisy urban environments. Furthermore, we show how to build consistent and efficient 2D representations out of 3D range data and how to detect poorly visible black vehicles. Experimental validation includes the most challenging conditions presented at the Urban Grand Challenge as well as other urban settings.

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

Access this article

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

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Blackman, S., Co, R., & El Segundo, C. (2004). Multiple hypothesis tracking for multiple target tracking. IEEE Aerospace and Electronic Systems Magazine, 19(1 Part 2), 5–18.

    Article  Google Scholar 

  • Blais, F. (2004). Review of 20 years of range sensor development. Journal of Electronic Imaging, 13, 231.

    Article  Google Scholar 

  • Buehler, M., Iagnemma, K., & Singh, S. (2007). The 2005 darpa grand challenge: The great robot race. New York: Springer.

    Book  Google Scholar 

  • Burgard, W., Fox, D., Hennig, D., & Schmidt, T. (1996). Estimating the absolute position of a mobile robot using position probability grids. In Proceedings of the national conference on artificial intelligence (pp. 896–901).

  • Darms, M., Rybski, P., Urmson, C., Inc, C., & Auburn Hills, M. (2008). Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments. In Intelligent vehicles symposium, 2008 (pp. 1197–1202). New York: IEEE.

    Chapter  Google Scholar 

  • DARPA (2007). Urban challenge rules, revision oct. 27, 2007. http://www.darpa.mil/grandchallenge/rules.asp.

  • Dellaert, F., & Thorpe, C. (1998). Robust car tracking using Kalman filtering and Bayesian templates. In Proceedings of SPIE (Vol. 3207, p. 72).

  • Dickmanns, E. (1998). Vehicles capable of dynamic vision: a new breed of technical beings? Artificial Intelligence, 103(1–2), 49–76.

    Article  MATH  Google Scholar 

  • Dietmayer, K., Sparbert, J., & Streller, D. (2001). Model based object classification and object tracking in traffic scenes from range images. In Proceedings of IV 2001, IEEE intelligent vehicles symposium (pp. 25–30).

  • Doucet, A., Freitas, Nd., Murphy, K., & Russell, S. (2000). Rao-blackwellised filtering for dynamic Bayesian networks. In Proceedings of the sixteenth conference on uncertainty in artificial intelligence, San Francisco, CA (pp. 176–183).

  • Fox, D., Burgard, W., & Thrun, S. (1999). Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 11(3), 391–427.

    MATH  Google Scholar 

  • Gerkey, B., Vaughan, R., & Howard, A. (2003). The player/stage project: Tools for multi-robot and distributed sensor systems. In Proceedings of the 11th international conference on advanced robotics (pp. 317–323).

  • Ibeo Automobile Sensor GmbH (2008). IBEO AlascaXT brochure. http://www.ibeo-as.com/english/press_downloads_brochures.asp.

  • Kabadayi, S., Pridgen, A., & Julien, C. (2006). Virtual sensors: Abstracting data from physical sensors. In Proceedings of the 2006 international symposium on world of wireless, mobile and multimedia networks (pp. 587–592). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Leonard, J., How, J., Teller, S., Berger, M., Campbell, S., Fiore, G., Fletcher, L., Frazzoli, E., Huang, A., Karaman, S. (2008). A perception-driven autonomous urban vehicle. Journal of Field Robotics, 25(10), 727–774. doi:10.1002/rob.20262.

    Article  Google Scholar 

  • Mäkynen, A. (2000). Position-sensitive devices and sensor systems for optical tracking and displacement sensing applications. PhD thesis, Department of Electrical Engineering, Oulu University, Oulu, Finland.

  • Mitchell, T., Hutchinson, R., Just, M., Newman, S., Stefan, R., Francisco, N., & Wang, P. X. (2002). Machine learning of fmri virtual sensors of cognitive states. In The 16th annual conference on neural information processing systems, computational neuroimaging: Foundations, concepts and methods workshop, 2002 (p. 87). Cambridge: MIT Press.

    Google Scholar 

  • Montemerlo, M. (2003). Fastslam: A factored solution to the simultaneous localization and mapping problem with unknown data association. PhD thesis, Robotics Institute, Carnegie Mellon University.

  • Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., Haehnel, D., Hilden, T., Hoffmann, G., Huhnke, B. (2008). Junior: The Stanford entry in the urban challenge. Journal of Field Robotics, 25(9), 569–597.

    Article  MATH  Google Scholar 

  • Moravec, H. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9(2), 61.

    Google Scholar 

  • Petrovskaya, A., & Thrun, S. (2008a). Efficient techniques for dynamic vehicle detection. In ISER, Athens, Greece.

  • Petrovskaya, A., & Thrun, S. (2008b). Model based vehicle tracking for autonomous driving in urban environments. In RSS, Zurich, Switzerland.

  • Petrovskaya, A., Khatib, O., Thrun, S., & Ng, A. Y. (2006). Bayesian estimation for autonomous object manipulation based on tactile sensors. In Robotics and automation. Proceedings of IEEE international conference (pp. 707–714).

  • Särkkä, S., Vehtari, A., & Lampinen, J. (2007). Rao-blackwellized particle filter for multiple target tracking. Information Fusion, 8(1), 2–15.

    Article  Google Scholar 

  • Schulz, D., Burgard, W., Fox, D., & Cremers, A. (2001). Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In IEEE international conference on robotics and automation, 2001. Proceedings 2001 ICRA (Vol. 2).

  • Sick Optics (2003). LMS 200/211/220/221/291 laser measurement systems technical description. http://www.sickusa.com/Publish/docroot/Manual(s)/LMSTechnicalDescription.pdf.

  • Srivastava, A., Oza, N., Stroeve, J., Aeronaut, N., Center, S., & Moffett Field, C. (2005). Virtual sensors: Using data mining techniques to efficiently estimate remote sensing spectra. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 590–600.

    Article  Google Scholar 

  • Streller, D., Furstenberg, K., & Dietmayer, K. (2002). Vehicle and object models for robust tracking in traffic scenes using laser range images. In The IEEE 5th international conference on intelligent transportation systems, 2002. Proceedings (pp. 118–123).

  • Thalmann, D., Noser, H., & Huang, Z. (1997). Autonomous virtual actors based on virtual sensors. Lecture Notes In Computer Science, 1195, 25–42.

    Article  Google Scholar 

  • Thrun, S. (2001). A probabilistic on-line mapping algorithm for teams of mobile robots. The International Journal of Robotics Research, 20(5), 335.

    Article  Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M., Dolan, J., Duggins, D., Galatali, T., Geyer, C. (2008). Autonomous driving in urban environments: Boss and the urban challenge. Journal of Field Robotics, 25(8), 425–466.

    Article  Google Scholar 

  • Velodyne Lidar, Inc. (2008). High definition lidar HDL-64E S2 specifications. http://www.velodyne.com/lidar/products/specifications.aspx.

  • Wang, C., Thorpe, C., Thrun, S., Hebert, M., & Durrant-Whyte, H. (2007). Simultaneous localization, mapping and moving object tracking. The International Journal of Robotics Research, 26, 889–916.

    Article  Google Scholar 

  • Wang, C. C. (2004). Simultaneous localization, mapping and moving object tracking. PhD thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

  • Wender, S., & Dietmayer, K. (2008). 3d vehicle detection using a laser scanner and a video camera. Intelligent Transport Systems, IET, 2(2), 105–112.

    Article  Google Scholar 

  • Zhao, L., & Thorpe, C. (1998). Qualitative and quantitative car tracking from a range image sequence. In 1998 IEEE computer society conference on computer vision and pattern recognition. Proceedings (pp. 496–501).

  • Zielke, T., Brauckmann, M., & von Seelen, W. (1993). Intensity and edge-based symmetry detection with an application to car-following. CVGIP: Image Understanding, 58(2), 177–190.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Stanford University, Palo Alto, USA

    Anna Petrovskaya & Sebastian Thrun

Authors
  1. Anna Petrovskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Thrun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Petrovskaya.

Additional information

This work was in part supported by the Defense Advanced Research Projects Agency under contract number HR0011-06-C-0145. The opinions expressed in the paper are ours and not endorsed by the U.S. Government.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Petrovskaya, A., Thrun, S. Model based vehicle detection and tracking for autonomous urban driving. Auton Robot 26, 123–139 (2009). https://doi.org/10.1007/s10514-009-9115-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-009-9115-1

Keywords

Search

Navigation