Search-Based Motion Planning for Performance Autonomous Driving
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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 https://git.io/JenvB.
KeywordsAutonomous 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.
- 2.Liniger, A., Lygeros, J.: A noncooperative game approach to autonomous racing. IEEE Trans. Control Syst. Technol. 1–14 (2019) Google Scholar
- 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.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
- 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
- 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.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.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
- 16.Pacejka, H.: Tire and Vehicle Dynamics. Butterworth-Heinemann, Oxford (2012)Google Scholar
- 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