Autonomous parking technology is a key concept within autonomous driving research. This paper will propose an imaginative autonomous parking algorithm to solve issues concerned with parking. The proposed algorithm consists of three parts: an imaginative model for anticipating results before parking, an improved rapid-exploring random tree (RRT) for planning a feasible trajectory from a given start point to a parking lot, and a path smoothing module for optimizing the efficiency of parking tasks. Our algorithm is based on a real kinematic vehicle model; which makes it more suitable for algorithm application on real autonomous cars. Furthermore, due to the introduction of the imagination mechanism, the processing speed of our algorithm is ten times faster than that of traditional methods, permitting the realization of real-time planning simultaneously. In order to evaluate the algorithm's effectiveness, we have compared our algorithm with traditional RRT, within three different parking scenarios. Ultimately, results show that our algorithm is more stable than traditional RRT and performs better in terms of efficiency and quality.
翻译:自动泊车技术是自主驾驶研究中的一个关键概念。 本文将提出一个富有想象力的自主泊车算法,以解决与泊车有关的问题。 提议的算法由三部分组成:一个在泊车前预测结果的富有想象力的模式,一个改进的快速勘探随机树(RRT),用于规划从一个特定起点到停车场的可行轨道,一个优化泊车工作效率的通路模块。我们的算法基于一个真正的运动型车辆模式,这使它更适合在真正的自动泊车上应用算法。此外,由于引入了想象力机制,我们算法的处理速度比传统方法快十倍,从而能够同时实现实时规划。为了评估算法的有效性,我们在三种不同的泊车场情景下将我们的算法与传统的RRT比较。 最终,结果显示我们的算法比传统的RRT更稳定,在效率和质量方面表现更好。