Skip to main navigation menu Skip to main content Skip to site footer

Articles

Vol. 5 (2018)

Motion Prediction and Risk Assessment for The Decision Making of Autonomous Vehicles

DOI
https://doi.org/10.31875/2409-9694.2018.05.5
Submitted
August 10, 2018
Published
10.08.2018

Abstract

The last few years, the automotive industry sees the Autonomous Vehicles (AV) as a great opportunity to increase comfort and road safety. One of the most challenging tasks is to detect dangerous situations and react to avoid or, at least, mitigate accidents. This requires a prediction of the evolution of the traffic surrounding the vehicle. This paper is a survey of the methods used in Automotive engineering for predicting future trajectories and collision risk assessment. models of vehicles are classified from the simplest to the more complexes. These technologies aim to improve road safety by estimating the level of dangerousness of a situation to make decision to avoid collision or mitigate its consequences.

References

  1. X. Qian, Model predictive control for autonomous and cooperative driving. Automatic Control Engineering. PSL Research University, 2016.
  2. J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli, "Kinematic and dynamic vehicle models for autonomous driving control design," in 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, South Korea, 2015; 1094-1099. https://doi.org/10.1109/IVS.2015.7225830
  3. F. Farroni, M. Russo, R. Russo, M. Terzo and F. Timpone," On the influence of anti-roll stiffness on vehicle stability and proposal of an innovative semi-active magnetorheological fluid anti-roll bar." In RAAD 2012: 21th International Workshop on Robotics inAlpe-Adria- Danube Region, 10-13 September 2012: Naples, Italy: Proceedings (p. 318). ESA.
  4. S. Ikenaga, FL. Lewis, J. Campos, and L. Davis, "Active suspension control of ground vehicle based on a full-vehicle model, "in Proceedings of the 2000 American Control Conference. ACC (IEEE Cat.No.00CH36334), Chicago, IL, USA, 2000; 6: 4019-4024. https://doi.org/10.1109/ACC.2000.876977
  5. C. Oertel, "On Modeling Contact and Friction Calculation of Tyre Response on Uneven Roads," Vehicle System Dynamics, 1997; 27(sup001): 289-302. https://doi.org/10.1080/00423119708969661
  6. R. Schubert, E. Richter and G. Wanielik," Comparison and evaluation of advanced motion models for vehicle tracking. In 2008 11th interna- tional conference on information fusion (pp.1-6). IEEE.
  7. M. Brannstrom, E. Coelingh, and J. Sjoberg, "Model-Based Threat Assessment for Avoiding Arbitrary Vehicle Collisions," IEEE Trans- actions on Intelligent Transportation Systems 2010; 11(3): 658-669. https://doi.org/10.1109/TITS.2010.2048314
  8. J. Hillenbrand, AM. Spieker, and K. Kroschel, "A Multilevel Collision Mitigation Approach - Its Situation Assessment, Decision Making, and Performance Tradeoffs, "IEEE Transactions on Intelligent Transportation Systems, 2006; 7(4): 528-540. https://doi.org/10.1109/TITS.2006.883115
  9. R. Miller and Qingfeng Huang, "An adaptive peer-to-peer collision warning system," in Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367), Birmingham, AL, USA, 2002; 1: 17-21.
  10. S. Ammoun and F. Nashashibi, "Real time trajectory prediction for collision risk estimation between vehicles," in 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 2009; 417-422. https://doi.org/10.1109/ICCP.2009.5284727
  11. A. Polychronopoulos, M. Tsogas, AJ. Amditis, and L. Andreone, "Sensor Fusion for Predicting Vehicles' Path for Collision Avoidance Systems," IEEE Transactions on Intelligent Transportation Systems 2007; 8(3): 549-562. https://doi.org/10.1109/TITS.2007.903439
  12. H. Veeraraghavan, N. Papanikolopoulos, and P. Schrater, "Determin- istic sampling-based switching kalman filtering for vehicle tracking," in 2006 IEEE Intelligent Transportation Systems Conference, Toronto, ON, Canada, 2006; 1340- 1345. https://doi.org/10.1109/ITSC.2006.1707409
  13. H. Dyckmanns, R. Matthaei, M. Maurer, B. Lichte, J. Effertz, and D. Stiker, "Object tracking in urban intersections based on active use of a priori knowledge: Active interacting multi model filter," in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, 2011; 625-630. https://doi.org/10.1109/IVS.2011.5940443
  14. A. Broadhurst, S. Baker, and T. Kanade, "Monte Carlo road safety reasoning," in IEEE Proceedings. Intelligent Vehicles Symposium, 2005, Las Vegas, NV, USA 2005; 319-324. https://doi.org/10.1109/IVS.2005.1505122
  15. A. Eidehall and L. Petersson, "Statistical Threat Assessment for Gen- eral Road Scenes Using Monte Carlo Sampling," IEEE Transactions on Intelligent Transportation Systems 2008; 9(1): 137-147. https://doi.org/10.1109/TITS.2007.909241
  16. GS. Aoude, VR. Desaraju, LH. Stephens, and JP. How, "Driver Behavior Classification at Intersections and Validation on Large Nat- uralistic Data Set," IEEE Transactions on Intelligent Transportation Systems 2012; 13(2): 724-736. https://doi.org/10.1109/TITS.2011.2179537
  17. M. Garcia Ortiz, J. Fritsch, F. Kummert, and A. Gepperth, "Behavior prediction at multiple time-scales in inner-city scenarios," in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany 2011; 1068-1073. https://doi.org/10.1109/IVS.2011.5940524
  18. B. Morris, A. Doshi, and M. Trivedi, "Lane change intent prediction for driver assistance: On-road design and evaluation," in 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany 2011; 895-901. https://doi.org/10.1109/IVS.2011.5940538
  19. HM. Mandalia and MDD. Salvucci," Using support vector machines for lane-change detection". In Proceedings of the human factors and ergonomics society annual meeting (Vol. 49, No. 22, pp. 1965-1969). Sage CA: Los Angeles, CA: SAGE Publications. https://doi.org/10.1177/154193120504902217
  20. H. Berndt, J. Emmert, and K. Dietmayer, "Continuous Driver Intention Recognition with Hidden Markov Models," in 2008 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China 2008; 1189-1194. https://doi.org/10.1109/ITSC.2008.4732630
  21. E. Ka¨fer, C. Hermes, C. Wo¨hler, H. Ritter, and F. Kummert, "Recog- nition of situation classes at road intersections," in 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 2010; 3960-3965. https://doi.org/10.1109/ROBOT.2010.5509919
  22. A. Lawitzky, D. Althoff, CF. Passenberg, G. Tanzmeister, D. Wollherr, and M. Buss, "Interactive scene prediction for automotive applications," in 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast City, Australia 2013; 1028- 1033. https://doi.org/10.1109/IVS.2013.6629601
  23. S. Lefèvre, D. Vasquez, and C. Laugier, "A survey on motion pre- diction and risk assessment for intelligent vehicles," ROBOMECH Journal 2014; 1(1). https://doi.org/10.1186/s40648-014-0001-z
  24. T. Christopher. (2009). Analysis of dynamic scenes: Application to driving assistance (Doctoral dissertation).
  25. C. Laugier, IE. Paromtchik, M. Perrollaz, M. Yong, JD. Yoder, C. Tay, K. Mekhnacha and A. Nègre, "Probabilistic analysis of dynamic scenes and collision risks assessment to improve driving safety." IEEE Intelligent Transportation Systems Magazine 2011; 3(4): 4-19. https://doi.org/10.1109/MITS.2011.942779
  26. S. Ammoun and F. Nashashibi, "Real time trajectory prediction for collision risk estimation between vehicles," in 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, 2009; 417-422. https://doi.org/10.1109/ICCP.2009.5284727
  27. MT. Wolf and JW. Burdick, "Artificial potential functions for highway driving with collision avoidance," in 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA 2008; 3731-3736. https://doi.org/10.1109/ROBOT.2008.4543783
  28. T. Brandt, T. Sattel, and J. Wallaschek, "On Automatic Collision Avoidance Systems," presented at the SAE 2005 World Congress & Exhibition, 2005. https://doi.org/10.4271/2005-01-1479
  29. J. Joseph, F. Doshi-Velez and N. Roy (2010)" A Bayesian nonparametric approach to modeling mobility patterns" In Proceedings of the AAAI conference on artificial intelligence, AAAI 2010, Atlanta, Georgia, USA 2010; 11-15.
  30. Q. Tran and J. Firl, "Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression," in 2014 IEEE Intelligent Vehicles Symposium Proceed- ings, MI, USA, 2014; 918- 923. https://doi.org/10.1109/IVS.2014.6856480