Spline-Based Dynamic Object Handling in Autonomous Vehicles: A Model-Based Path Planning Algorithm

Aliki Stefanopoulou, S. Gkelios, Athanasios Ch. Kapoutsis, Elias B. Kosmatopoulos, Y. Boutalis
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Abstract

In this study we propose a model-based dynamic path planning algorithm that is designed to navigate Autonomous Vehicles through complex and dynamic environments. To achieve that, a novel spline-based approach is utilized for the production of several candidate paths along a predetermined route and a Gaussian-based function is utilized for their evaluation. Our algorithm takes into account various factors, such as static and dynamic objects, to make the appropriate decisions for the vehicle’s path, making it a promising solution for such objects during an autonomous vehicle navigation. The algorithm was tested in high-fidelity scenarios using CARLA Simulator, which is a powerful tool for simulating autonomous vehicle scenarios. The results indicate that the proposed algorithm is capable of generating efficient and safe paths for the vehicle to follow.
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基于样条的自动驾驶车辆动态目标处理:一种基于模型的路径规划算法
在这项研究中,我们提出了一种基于模型的动态路径规划算法,旨在引导自动驾驶汽车通过复杂和动态的环境。为了实现这一目标,利用一种新的基于样条的方法沿预定路线产生若干候选路径,并利用基于高斯的函数对其进行评估。我们的算法考虑了各种因素,如静态和动态物体,为车辆的路径做出适当的决策,使其成为自动驾驶车辆导航过程中此类物体的有希望的解决方案。利用CARLA模拟器在高保真场景下对算法进行了测试,该模拟器是模拟自动驾驶汽车场景的强大工具。结果表明,该算法能够生成高效、安全的路径供车辆跟随。
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