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.
note footnote at the bottom: "http://www.sciencemag.org/content/313/5786/504.abstract, http://www.cs.toronto.edu/~amnih/cifar/talks/salakhut_talk.pdf. In a strict sense, this work was obsoleted by a slew of papers from 2011 which showed that you can achieve similar results to this 2006 result with “simple” algorithms, but it’s still true that current deep learning methods are better than the best “simple” feature learning schemes, and this paper was the first example that came to mind. [return]"
J. Lin, R. Nogueira, und A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.