References
Fleming J M, Allison C K, Yan X D, et al. Adaptive driver modelling in ADAS to improve user acceptance: a study using naturalistic data. Saf Sci, 2019, 119: 76–83
Bärgman J, Boda C N, Dozza M. Counterfactual simulations applied to SHRP2 crashes: the effect of driver behavior models on safety benefit estimations of intelligent safety systems. Accid Anal Prev, 2017, 102: 165–180
Chen S T, Jian Z Q, Huang Y H, et al. Autonomous driving: cognitive construction and situation understanding. Sci China Inf Sci, 2019, 62: 081101
Chen T Q, Guestrin C, Yan X. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016. 785–794
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9: 1735–1780
Acknowledgements
This work was supported by Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education, China (Grant No. 2019KLMT05) and Natural Science Foundation of Chongqing (Grant No. cstc2019jcyj-msxmX0119).
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Lou, B., Li, Y., Lu, X. et al. Car-following behavior modeling driven by small data sets based on mnemonic extreme gradient boosting framework. Sci. China Inf. Sci. 65, 169203 (2022). https://doi.org/10.1007/s11432-020-3044-6
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DOI: https://doi.org/10.1007/s11432-020-3044-6