Vehicle path planning with maximizing safe margin for driving using Lagrange multipliers
Quoc Huy, Hossein Tehrani Nik Nejad, Keisuke Yoneda, Sakai Ryohei, Seiichi Mita · 2013
We propose a path planning method for autonomous vehicle in cluttered environment with narrow passages. Different from traditional methods, we use a learning approach based on RBF kernel SVM to maximize the safety margin for driving. We use the Lagrange multipliers of SVM dual model to find most critical points in map and generate optimized hyperplane for path. The method is implemented on autonomous vehicle for outdoor parking and compared to well-known method in autonomous vehicle literatures. The experiments prove that the method is able to generate smooth and safe path in shorter time compared to other methods.