Real-time optimal motion planning for automated road vehicles⁎⁎EFOP-3.6.3-VeKOP-16-2017-00001: Talent management in autonomous vehicle control technologies - The Project is supported by the Hungarian Government and co-financed by the European Social Fund.⁎⁎⁎⁎The research reported in this paper was supported by the Higher education excellence Program in the frame of Artificial Intelligence research area of Budapest University of Technology and economics (BMe FIKP-MI/FM).

This paper presents a real-time optimal motion planner algorithm for road vehicles. The method is based on a cubic spline trajectory planner which is able to plan a set of vehicle motions driving from a given initial state to a required final state. Maximal dynamical feasibility and passenger comfort are ensured by minimizing the lateral acceleration and tracking errors as the vehicle moves along the trajectory. Tracking of the planned motion is realized during planning and execution as well by separate longitudinal and lateral controllers. efficient implementation and small number of optimization variables enables real-time usage. The trajectory planner is first tested in a quasi real-time simulation environment and then under real working conditions at the dynamic platform of proving ground ZalaZone with a completely drive-by-wire Smart Fortwo. Measurement results are presented and analyzed in detail, and possible future research directions are mentioned.