Investigating the dynamic memory effect of human drivers via ON-LSTM | |
Dai, Shengzhe1,2; Li, Zhiheng1,2; Li, Li1; Cao, Dongpu3; Dai, Xingyuan4; Lin, Yilun4 | |
发表期刊 | SCIENCE CHINA-INFORMATION SCIENCES |
ISSN | 1674-733X |
2020-08-13 | |
卷号 | 63期号:9页码:11 |
摘要 | It is a widely accepted view that considering the memory effects of historical information (driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However, many commonly used models (e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range (ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM (ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions. |
关键词 | driving behavior memory effect trajectory prediction historical information ON-LSTM |
DOI | 10.1007/s11432-019-2844-3 |
关键词[WOS] | CAR ; RECOGNITION ; STABILITY |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018AAA0101400] ; National Natural Science Foundation of China[61790565] ; Science and Technology Innovation Committee of Shenzhen[JCYJ20170818092931604] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Science and Technology Innovation Committee of Shenzhen ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000564323200001 |
出版者 | SCIENCE PRESS |
七大方向——子方向分类 | 平行管理与控制 |
国重实验室规划方向分类 | 实体人工智能系统决策-控制 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41534 |
专题 | 复杂系统管理与控制国家重点实验室 |
通讯作者 | Li, Li |
作者单位 | 1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China 2.Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Peoples R China 3.Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China |
推荐引用方式 GB/T 7714 | Dai, Shengzhe,Li, Zhiheng,Li, Li,et al. Investigating the dynamic memory effect of human drivers via ON-LSTM[J]. SCIENCE CHINA-INFORMATION SCIENCES,2020,63(9):11. |
APA | Dai, Shengzhe,Li, Zhiheng,Li, Li,Cao, Dongpu,Dai, Xingyuan,&Lin, Yilun.(2020).Investigating the dynamic memory effect of human drivers via ON-LSTM.SCIENCE CHINA-INFORMATION SCIENCES,63(9),11. |
MLA | Dai, Shengzhe,et al."Investigating the dynamic memory effect of human drivers via ON-LSTM".SCIENCE CHINA-INFORMATION SCIENCES 63.9(2020):11. |
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