Safe Path Planning with Gaussian Process Regulated Risk Map
Hongliang Guo, Zehui Meng, Zefan Huang, Leong Wei Kang, Ziyue Chen, Malika Meghjani, Marcelo Ang, Daniela Rus
Abstract
Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safety during navigation. However, in reality, AV safety has been far below its expectation, and so far, no government has allowed for complete autonomous driving without human supervision. This paper proposes a dynamic safe path planning algorithm for AVs with Gaussian process regulated risk map. By reasonably assuming that the output of the object detection and tracking module follows a multi-variate Gaussian distribution, we put forward a safe path planning paradigm with Gaussian process regulated risk map, ensuring safety with high confidence. Both simulation results and in-vehicle tests demonstrate the effectiveness of the proposed algorithm.
BibTeX
@inproceedings{iros2019_safepathplanning,
title = {Safe Path Planning with Gaussian Process Regulated Risk Map},
author = {Hongliang Guo and Zehui Meng and Zefan Huang and Leong Wei Kang and Ziyue Chen and Malika Meghjani and Marcelo Ang and Daniela Rus},
booktitle = {IROS 2019},
year = {2019}
}