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Hierarchical Design of Highway Merging Controller Using Navigation Vector Fields Under Bounded Sensing Uncertainty

  • Lixing HuangEmail author
  • Dimitra Panagou
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 9)

Abstract

This paper presents a hierarchical control design for the motion of an autonomous car (ego-vehicle) through traffic on a highway. The ego-vehicle is assumed to sense the position and speed of the surrounding cars with bounded errors, and its objective is to move safely among traffic. The design is composed of a low-level tracking controller and a high-level decision-making process: the low-level controller is based on a navigation vector field and a velocity controller that safely drive the ego-car to selected merging points. The merging points are decided based upon a cost function capturing the traffic conditions. Simulation results demonstrate the efficacy of the proposed algorithm.

Keywords

Safe merging in highways Collision avoidance 

Notes

Acknowledgements

Toyota Research Institute (TRI) provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity.

References

  1. 1.
    Alonso-Mora, J., Breitenmoser, A., Beardsley, P., Siegwart, R.: Reciprocal collision avoidance for multiple car-like robots. In: 2012 IEEE International Conference on Robotics and Automation, pp. 360–366 (May 2012)Google Scholar
  2. 2.
    Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation, 2008, pp. 1928–1935. ICRA 2008. IEEE (2008)Google Scholar
  3. 3.
    Carvalho, A., Gao, Y., Lefevre, S., Borrelli, F.: Stochastic predictive control of autonomous vehicles in uncertain environments. In: 12th International Symposium on Advanced Vehicle Control (2014)Google Scholar
  4. 4.
    Cesari, G., Schildbach, G., Carvalho, A., Borrelli, F.: Scenario model predictive control for lane change assistance and autonomous driving on highways. IEEE Intell. Transp. Syst. Mag. 9(3), 23–35 (2017)CrossRefGoogle Scholar
  5. 5.
    Chen, Y., Peng, H., Grizzle, J.: Obstacle avoidance for low-speed autonomous vehicles with barrier function. IEEE Trans. Control. Syst. Technol. 26(1), 194–206 (2018)CrossRefGoogle Scholar
  6. 6.
    Falcone, P., Eric Tseng, H., Borrelli, F., Asgari, J., Hrovat, D.: Mpc-based yaw and lateral stabilisation via active front steering and braking. Veh. Syst. Dyn. 46(S1), 611–628 (2008)CrossRefGoogle Scholar
  7. 7.
    Feller, C., Ebenbauer, C.: Weight recentered barrier functions and smooth polytopic terminal set formulations for linear model predictive control. In: American Control Conference (ACC), 2015, pp. 1647–1652. IEEE (2015)Google Scholar
  8. 8.
    Gipps, P.G.: A model for the structure of lane-changing decisions. Transp. Res. Part B Methodol. 20(5), 403–414 (1986)CrossRefGoogle Scholar
  9. 9.
    Han, D., Huang, L., Panagou, D.: Approximating the region of multi-task coordination via the optimal lyapunov-like barrier function (2018). arXiv:1802.09921
  10. 10.
    Huang, L., Panagou, D.: Automated turning and merging for autonomous vehicles using a nonlinear model predictive control approach. In: American Control Conference (ACC), 2017, pp. 5525–5531. IEEE (2017)Google Scholar
  11. 11.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefGoogle Scholar
  12. 12.
    LaValle, S.M., Kuffner Jr., J.J.: Rapidly-exploring random trees: progress and prospects (2000)Google Scholar
  13. 13.
    Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J.Z., Langer, D., Pink, O., Pratt, V., Sokolsky, M., Stanek, G., Stavens, D., Teichman, A., Werling, M., Thrun, S.: Towards fully autonomous driving: systems and algorithms. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 163–168 (June 2011)Google Scholar
  14. 14.
    Li, J., Sun, X.: A route planning’s method for unmanned aerial vehicles based on improved a-star algorithm. Acta Armamentarii 7, 788–792 (2008)Google Scholar
  15. 15.
    Ma, X., Jiao, Z., Wang, Z., Panagou, D.: 3-d decentralized prioritized motion planning and coordination for high-density operations of micro aerial vehicles. IEEE Trans. Control. Syst. Technol. 26(3), 939–953 (May 2018)CrossRefGoogle Scholar
  16. 16.
    Nilsson, P., Hussien, O., Chen, Y., Balkan, A., Rungger, M., Ames, A., Grizzle, J., Ozay, N., Peng, H., Tabuada, P.: Preliminary results on correct-by-construction control software synthesis for adaptive cruise control, pp. 816–823 (Dec 2014)Google Scholar
  17. 17.
    Nilsson, P., Hussien, O., Balkan, A., Chen, Y., Ames, A.D., Grizzle, J.W., Ozay, N., Peng, H., Tabuada, P.: Correct-by-construction adaptive cruise control: two approaches. IEEE Trans. Control. Syst. Technol. 24(4), 1294–1307 (2016)CrossRefGoogle Scholar
  18. 18.
    Ort, T., Paull, L., Rus, D.: Autonomous vehicle navigation in rural environments without detailed prior mapsGoogle Scholar
  19. 19.
    Panagou, D.: A distributed feedback motion planning protocol for multiple unicycle agents of different classes. IEEE Trans. Autom. Control. 62(3), 1178–1193 (March 2017)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Panagou, D., Stipanovic, D.M., Voulgaris, P.G.: Multi-objective control for multi-agent systems using lyapunov-like barrier functions. In: 52nd IEEE Conference on Decision and Control, pp. 1478–1483 (Dec 2013)Google Scholar
  21. 21.
    Rai, R., Sharma, B., Vanualailai, J.: Real and virtual leader-follower strategies in lane changing, merging and overtaking maneuvers. In: 2015 2nd Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pp. 1–12. IEEE (2015)Google Scholar
  22. 22.
    Wills, A., Heath, W.: A recentred barrier for constrained receding horizon control. In: American Control Conference, 2002. Proceedings of the 2002, vol. 5, pp. 4177–4182. IEEE (2002)Google Scholar
  23. 23.
    Zhou, D., Wang, Z., Bandyopadhyay, S., Schwager, M.: Fast, on-line collision avoidance for dynamic vehicles using buffered voronoi cells. IEEE Robot. Autom. Lett. 2(2), 1047–1054 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Robotics InstituteUniversity of MichiganAnn ArborUSA
  2. 2.Aerospace EngineeringUniversity of MichiganAnn ArborUSA

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