Local Path Generation Method for Unmanned Autonomous Vehicles Using Reinforcement Learning


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 9, pp. 369-374, Sep. 2014
10.3745/KTSDE.2014.3.9.369,   PDF Download:

Abstract

Path generation methods are required for safe and efficient driving in unmanned autonomous vehicles. There are two kinds of paths: global and local. A global path consists of all the way points including the source and the destination. A local path is the trajectory that a vehicle needs to follow from a way point to the next in the global path. In this paper, we propose a novel method for local path generation through machine learning, with an effective curve function used for initializing the trajectory. First, reinforcement learning is applied to a set of candidate paths to produce the best trajectory with maximal reward. Then the optimal steering angle with respect to the trajectory is determined by training an artificial neural network. Our method outperformed existing approaches and successfully found quality paths in various experimental settings, including the cases with obstacles.


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Cite this article
[IEEE Style]
M. J. Kim, K. C. Choi, B. H. Oh, J. H. Yang, "Local Path Generation Method for Unmanned Autonomous Vehicles Using Reinforcement Learning," KIPS Transactions on Software and Data Engineering, vol. 3, no. 9, pp. 369-374, 2014. DOI: 10.3745/KTSDE.2014.3.9.369.

[ACM Style]
Moon Jong Kim, Ki Chang Choi, Byong Hwa Oh, and Ji Hoon Yang. 2014. Local Path Generation Method for Unmanned Autonomous Vehicles Using Reinforcement Learning. KIPS Transactions on Software and Data Engineering, 3, 9, (2014), 369-374. DOI: 10.3745/KTSDE.2014.3.9.369.