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Probabilistic Vehicle Motion Modeling and Risk Estimation

  • Christopher Tay
  • Kamel Mekhnacha
  • Christian Laugier

Abstract

The development of autonomous vehicles garnered an increasing amount of attention in recent years. The interest for automotive industries is to produce safer and more user-friendly cars. A common reason behind most traffic accidents is the failure on the part of the driver to adequately monitor the vehicle’s surroundings. This chapter addresses the problem of estimating the collision risk for a vehicle for the next few seconds in urban traffic conditions.

Current commercially available crash warning systems are usually equipped with radar-based sensors on the front, rear, or sides to measure the velocity and distance to obstacles. The algorithms for determining the risk of collision are based on variants of time-to-collision (TTC). However, it might be misleading in situations where the roads are curved and the assumption that motion is linear does not hold. In these situations, the risk tends to be underestimated. Furthermore, instances of roads which are not straight can be commonly found in urban environments, like the roundabout or cross-junctions.

An argument of this chapter is that simply knowing that there is an object at a certain location at a specific instance in time does not provide sufficient information to assess its safety. A framework for understanding behaviors of vehicle motion is indispensable. In addition, environmental constraints should be taken into account especially for urban traffic environments.

This chapter proposes a complete probabilistic model motion at the trajectory level based on the Gaussian Process (GP). Its advantage over current methods is that it is able to express future motion independently of state space discretization. Driving behaviors are modeled with a variant of the Hidden Markov Model. The combination of these two models provides a complete probabilistic model for vehicle evolution in time. Additionally a general method of probabilistically evaluating collision risk is presented, where different forms of risk values with different semantics can be obtained, depending on its applications.

Keywords

Hide Markov Model Gaussian Process Hide State Autonomous Vehicle Lane Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Brand M, Kettnaker V (2000) Discovery and segmentation of activities in video. IEEE Trans Pattern Anal Mach Intell 22(8):844–851CrossRefGoogle Scholar
  2. Brand M, Oliver N, Pentland A (1997) Coupled hidden markov models for complex action recognition. MIT Media Lab, Cambridge, MA, pp 994–999Google Scholar
  3. Eck M, DeRose T, Duchamp T, Hoppe H, Lounsbery M, Stuetzle W (1995) Multiresolution analysis of arbitrary meshes. In SIGGRAPH’95: Proceedings of the 22nd annual conference on computer graphics and interactive techniques. ACM, New York, pp 173–182Google Scholar
  4. Fuerstenberg K, Chen J (2007) New european approach for intersection safety – results of the ec project intersafe. In: Proceedings of the international forum on advanced microsystems for automotive application. Springer, Berlin Heidelberg, New York, pp 61–74Google Scholar
  5. Galata A, Johnson N, Hogg D (2001) Learning variable length markov models of behavior. Comput Vis Image Und 81:398–413MATHCrossRefGoogle Scholar
  6. Hestenes MR, Stiefel E (1952) Methods of conjugate gradients for solving linear systems. J Res Natl Bur Stand 49:409–436MathSciNetMATHGoogle Scholar
  7. Hongeng S, Brmond F, Nevatia R (2000) Representation and optimal recognition of human activities. In: Proceedings of the IEEE conference on computer vision and pattern recognition, CVPR 2000, IEEE, Hilton Head Island SC, USA, pp 818–825Google Scholar
  8. Lee DN (1976) A theory of visual control of braking based on information about time-to-collision. Perception 5(4):437–459, EdinburghCrossRefGoogle Scholar
  9. Lvy B, Petitjean S, Ray N, Maillot J (2002) Least squares conformal maps for automatic texture atlas generation. In: ACM SIGGRAPH proceedings of the 29th annual conference on computer graphics and interactive techniques. ACM, New York, Jul 2002Google Scholar
  10. National Highway Traffic Safety Administration (2004) Vehicle-based countermeasures for signal and stop sign violation. Technical report DOT HS 809 716, NHTSA, U.S. DOTGoogle Scholar
  11. Oliver N, Horvitz E, Garg A (2002) Layered representations for human activity recognition. In: Proceedings fourth ieee international conference on multimodal interfaces, pp 3–8Google Scholar
  12. Pierowicz J, Jocoy E, Lloyd M, Bittner A, Pirson B (2000) Intersection collision avoidance using its countermeasures. Technical report DOT HS 809 171, NHTSA, U.S. DOTGoogle Scholar
  13. Pinkall U, Des Juni S, Polthier K (1993) Computing discrete minimal surfaces and their conjugates. Exp Math 2:15–36MATHGoogle Scholar
  14. Rabiner L (1989) A tutorial on hmm and selected applications in speech recognition. Proc IEEE 77(2):257–286CrossRefGoogle Scholar
  15. TAY C (2009) Analysis of dynamic scenes: application to driving assistance. PhD thesis, Institut National Polytechnique de Grenoble, France, Sept 2009Google Scholar
  16. Wilson AD, Bobick AF (1998) Recognition and interpretation of parametric gesture. In: Sixth International Conference on Computer Vision, IEEE, Bombay, India, pp 329–336, Jan 1998Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.Probayes SASInovalée Saint Ismier CedexFrance
  2. 2.e-Motion Project-TeamINRIA Grenoble Rhône-AlpesSaint Ismier CedexFrance

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