作者
Seong Hyeon Park, ByeongDo Kim, Chang Mook Kang, Chung Choo Chung, Jun Won Choi
发表日期
2018/6/26
研讨会论文
2018 IEEE intelligent vehicles symposium (IV)
页码范围
1672-1678
出版商
IEEE
简介
In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the K most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the K locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques.
学术搜索中的文章
SH Park, BD Kim, CM Kang, CC Chung, JW Choi - 2018 IEEE intelligent vehicles symposium (IV), 2018