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Dynamic and Safe Path Planning Based on Support Vector Machine among Multi Moving Obstacles for Autonomous Vehicles
Quoc Huy DO Seiichi MITA Hossein Tehrani Nik NEJAD Long HAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/02/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
path planning, support vector machine, particle filter, Bezier curve,
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We propose a practical local and global path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semi-structured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm utilizes a probabilistic method based on particle filters to estimate the dynamic obstacles' locations, a support vector machine to provide the critical points and Bezier curves to smooth the generated path. The generated path safely travels through various static and moving obstacles and satisfies the vehicle's movement constraints. The algorithm is implemented and verified on simulation software. Simulation results demonstrate the effectiveness of the proposed method in complicated scenarios that posit the existence of multi moving objects.