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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Abstract

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.

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2018AAA0101400), National Natural Science Foundation of China (Grant No. 61790565), Science and Technology Innovation Committee of Shenzhen (Grant No. JCYJ20170818092931604), and Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (Grant No. ICRI-IACV).

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Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, China

    Shengzhe Dai, Zhiheng Li & Li Li

  2. Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

    Shengzhe Dai & Zhiheng Li

  3. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, N2L 3G1, Canada

    Dongpu Cao

  4. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China

    Xingyuan Dai & Yilun Lin

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  1. Shengzhe Dai
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  2. Zhiheng Li
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  3. Li Li
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  4. Dongpu Cao
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  5. Xingyuan Dai
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  6. Yilun Lin
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Correspondence to Li Li.

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Dai, S., Li, Z., Li, L. et al. Investigating the dynamic memory effect of human drivers via ON-LSTM. Sci. China Inf. Sci. 63, 190202 (2020). https://doi.org/10.1007/s11432-019-2844-3

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  • DOI: https://doi.org/10.1007/s11432-019-2844-3

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