Abstract
Several studies have demonstrated that car dynamics computation is essential for autonomous car motion planning. One of the most promising techniques for motion planning with dynamics is model predictive control (MPC). Planned motion computed using MPC consists of solving an optimization problem with constraint equations representing car dynamics and environmental conditions. The disadvantage is that the optimization problem is complex when it is nonlinear. To overcome this, we have developed a highly efficient computing technique for autonomous car motion planning with nonlinear MPC (NMPC). It consists of an approximated problem and a sequential quadratic problem based on an active-set algorithm. We demonstrate the suitability of our approach by using a car dynamics simulator.
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