Model-based Decision Making with Imagination for Autonomous Parking

This paper has been accepted by IEEE Symposium on Intelligent Vehicle (IV) 2018.

By Ziyue Feng, Shitao Chen, Yu Chen, Nanning Zheng.

IEEE Xplore: Link

Arxiv: Link

Abstract

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.

Requirements

• OpenCV
• LEDA

Simple Video Demo

Citation

@INPROCEEDINGS{8500700,
  author={Feng, Ziyue and Chen, Shitao and Chen, Yu and Zheng, Nanning},
  booktitle={2018 IEEE Intelligent Vehicles Symposium (IV)}, 
  title={Model-Based Decision Making With Imagination for Autonomous Parking}, 
  year={2018},
  volume={},
  number={},
  pages={2216-2223},
  doi={10.1109/IVS.2018.8500700}}

Reference

This project is developed from Nonholonomic Mobile Robot Motion Planning

Contact

If you have any question or concerns about this paper or repository, please feel free to contact Ziyue Feng (zfeng@clemson.edu).