Search-Based Motion Planning for Performance Autonomous Driving

Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures. Our code is available at


Autonomous vehicles Trail-braking Drifting Motion planning 



The project leading to this study has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 675999, ITEAM project. VIRTUAL VEHICLE Research Center is funded within the COMET - Competence Centers for Excellent Technologies - programme by the Austrian Federal Ministry for Transport, Innovation and Technology (BMVIT), the Federal Ministry of Science, Research and Economy (BMWFW), the Austrian Research Promotion Agency (FFG), the province of Styria and the Styrian Business Promotion Agency (SFG). The COMET programme is administrated by FFG.


  1. 1.
    Liniger, A., Domahidi, A., Morari, M.: Optimization-based autonomous racing of 1:43 scale RC cars. Optimal Control Appl. Methods 36(5), 628–647 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Liniger, A., Lygeros, J.: A noncooperative game approach to autonomous racing. IEEE Trans. Control Syst. Technol. 1–14 (2019) Google Scholar
  3. 3.
    Kolter, J.Z., Plagemann, C., Jackson, D.T., Ng, A.Y., Thrun, S.: A probabilistic approach to mixed open-loop and closed-loop control, with application to extreme autonomous driving. In: 2010 IEEE International Conference on Robotics and Automation, pp. 839–845. IEEE (2010)Google Scholar
  4. 4.
    Velenis, E., Tsiotras, P., Lu, J.: Modeling aggressive maneuvers on loose surfaces: the cases of trail-braking and pendulum-turn. In: ECC, pp. 1233–1240. IEEE (2007)Google Scholar
  5. 5.
    Velenis, E., Tsiotras, P., Lu, J.: Optimality properties and driver input parameterization for trail-braking cornering. Eur. J. Control 14(4), 308–320 (2008)CrossRefGoogle Scholar
  6. 6.
    Tavernini, D., Massaro, M., Velenis, E., Katzourakis, D.I., Lot, R.: Minimum time cornering: the effect of road surface and car transmission layout. Veh. Syst. Dyn. 51(10), 1533–1547 (2013)CrossRefGoogle Scholar
  7. 7.
    You, C., Tsiotras, P.: Real-time trail-braking maneuver generation for off-road vehicle racing. In: 2018 Annual American Control Conference (ACC), pp. 4751–4756. IEEE (2018)Google Scholar
  8. 8.
    Zhang, F., Gonzales, J., Li, S.E., Borrelli, F., Li, K.: Drift control for cornering maneuver of autonomous vehicles. Mechatronics 54, 167–174 (2018)CrossRefGoogle Scholar
  9. 9.
    Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J.M., Boots, B., Theodorou, E.A.: Information theoretic MPC for model-based reinforcement learning. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1714–1721, May 2017Google Scholar
  10. 10.
    Ajanovic, Z., Lacevic, B., Shyrokau, B., Stolz, M., Horn, M.: Search-based optimal motion planning for automated driving. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4523–4530. IEEE (2018)Google Scholar
  11. 11.
    Kuwata, Y., Teo, J., Karaman, S., Fiore, G., Frazzoli, E., How, J.: Motion planning in complex environments using closed-loop prediction. In: AIAA Guidance, Navigation and Control Conference and Exhibit, p. 7166 (2008)Google Scholar
  12. 12.
    Regolin, E., Zambelli, M., Ferrara, A.: A multi-rate ISM approach for robust vehicle stability control during cornering. IFAC-PapersOnLine 51(9), 249–254 (2018)CrossRefGoogle Scholar
  13. 13.
    Regolin, E., Vazquez, A.G.A., Zambelli, M., Victorino, A., Charara, A., Ferrara, A.: A sliding mode virtual sensor for wheel forces estimation with accuracy enhancement via EKF. IEEE Trans. Veh. Technol. 68(4), 3457–3471 (2019)CrossRefGoogle Scholar
  14. 14.
    Genta, G.: Motor Vehicle Dynamics: Modeling and Simulation, vol. 43. World Scientific, Singapore (1997)CrossRefGoogle Scholar
  15. 15.
    Velenis, E., Katzourakis, D., Frazzoli, E., Tsiotras, P., Happee, R.: Steady-state drifting stabilization of RWD vehicles. Control Eng. Pract. 19(11), 1363–1376 (2011)CrossRefGoogle Scholar
  16. 16.
    Pacejka, H.: Tire and Vehicle Dynamics. Butterworth-Heinemann, Oxford (2012)Google Scholar
  17. 17.
    Werling, M., Kammel, S., Ziegler, J., Gröll, L.: Optimal trajectories for time-critical street scenarios using discretized terminal manifolds. Int. J. Robot. Res. 31(3), 346–359 (2012)CrossRefGoogle Scholar
  18. 18.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4(2), 100–107 (1968)CrossRefGoogle Scholar
  19. 19.
    Montemerlo, M., et al.: Junior: the stanford entry in the urban challenge. J. Field Robot. 25(9), 569–597 (2008)CrossRefGoogle Scholar
  20. 20.
    Ziegler, J., Bender, P., Dang, T., Stiller, C.: Trajectory planning for Bertha — a local, continuous method. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, 8–11 June 2014, pp. 450–457. IEEE (2014)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Virtual Vehicle Research CenterGrazAustria
  2. 2.Dipartimento di Ingegneria Industriale e dell’InformazioneUniversity of PaviaPaviaItaly
  3. 3.Graz University of TechnologyGrazAustria

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