No abstract

No abstract

Maneuvering autonomous vehicles in constrained environments, such as autonomous vehicle parking, is not a trivial task and has received increasing attention from both the academy and industry. However, the traditional methods divide the problem into parallel parking, perpendicular parking, and echelon parking, then different methods are applied for the parking motion planning. In this paper a Rapidly-exploring Random Tree (RRT) based path planner is implemented for autonomous vehicle parking problem, which treats all the situations in a unified manner. As the RRT method sometimes generates some complicated paths, a smoother is also implemented for smoothing generated paths. The proposed algorithm is verified in simulation and generates applicable solutions for the proposed application scenarios.

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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 semistructured (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 Bézier 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.