Investigating the dynamic memory effect of human drivers via ON-LSTM
文献类型：期刊论文
| 作者 | Dai, Shengzhe1,2; Li, Zhiheng1,2; Li, Li1; Cao, Dongpu3; Dai, Xingyuan4 ; Lin, Yilun4
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| 刊名 | SCIENCE CHINA-INFORMATION SCIENCES
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| 出版日期 | 2020-08-13 |
| 卷号 | 63期号:9页码:11 |
| 关键词 | driving behavior memory effect trajectory prediction historical information ON-LSTM |
| ISSN号 | 1674-733X |
| DOI | 10.1007/s11432-019-2844-3 |
| 通讯作者 | Li, Li(li-li@tsinghua.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | CAR ; RECOGNITION ; STABILITY |
| 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 |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000564323200001 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41533]  |
| 专题 | 自动化研究所_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. |
入库方式： OAI收割
来源：自动化研究所
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