Abstract
Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion; the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation (NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNN-LSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.
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References
He W, Zhang S. Control design for nonlinear flexible wings of a robotic aircraft. IEEE Trans Contr Syst Technol, 2017, 25: 351–357
Cao Y, Ma L, Zhang Y. Application of fuzzy predictive control technology in automatic train operation. Cluster Comput, 2019, 22: 14135–14144
Yu W K, Zhao C H. Online fault diagnosis for industrial processes with Bayesian network-based probabilistic ensemble learning strategy. IEEE Trans Automat Sci Eng, 2019, 16: 1922–1932
Chai Z, Zhao C H. A fine-grained adversarial network method for cross-domain industrial fault diagnosis. IEEE Trans Automat Sci Eng, 2020. doi: https://doi.org/10.1109/TASE.2019.2957232
Cao Y, Zhang Y Z, Wen T, et al. Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system. Chaos, 2019, 29: 013130
Xie G, Peng X, Li X, et al. Remaining useful life prediction of lithium-ion battery based on an improved particle filter algorithm. Can J Chem Eng, 2019, 41: 23675
Xie G, Li X, Peng X, et al. Estimating the probability density function of remaining useful life for Wiener degradation process with uncertain parameters. Int J Control Autom Syst, 2019, 17: 2734–2745
Xie G, Jin Y Z, Hei X H, et al. Adaptive identification of time-varying environmental parameters in train dynamics model. Acta Autom Sin, 2020. doi: https://doi.org/10.16383/j.aas.c190215
Qiao S J, Jin K, Han N, et al. Trajectory prediction algorithm based on gaussian mixture model. J Softw, 2015, 26: 1048–1063
Qiao S J, Shen D Y, Wang X T, et al. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Trans Intell Transp Syst, 2015, 16: 284–296
Wu P J, Yang W T, Yu C, et al. Trajectory prediction method for high precision servo control system (in Chinese). Electric Mach Control, 2014, 18: 1–5
Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle trajectory prediction based on motion model and maneuver recognition. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, 2013. 4363–4369
Fei R, Li S S, Hei X H, et al. A motion simulation model for road network based crowdsourced map datum. J Intell Fuzzy Syst, 2020, 38: 391–407
Xie G, Sun L L, Wen T, et al. Adaptive transition probability matrix-based parallel IMM algorithm. IEEE Trans Syst Man Cybern Syst, 2019. doi: https://doi.org/10.1109/TSMC.2019.2922305
Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436
Li D Y, Liu M, Zhao F, et al. Challenges and countermeasures of interaction in autonomous vehicles. Sci China Inf Sci, 2019, 62: 050201
Deo N, Trivedi M M. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), Changshu, 2018
Park S H, Kim B D, Kang C M, et al. Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), Changshu, 2018
Altché F, Arnaud D L F. An LSTM network for highway trajectory prediction. In: Proceedings of IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017. 353–359
Zhang P, Yang T, Liu Y N, et al. QAR data feature extraction and prediction based on CNN-LSTM (in Chinese). Appl Res Comput, 2019, 36: 2958–2961
Kim B D, Kang C M, Lee S H, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In: Proceedings of IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017. 399–404
Yang J, Xie G, Yang Y X, et al. Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis. ISA Trans, 2019, 95: 306–319
Yang J, Xie G, Yang Y X, et al. An improved deep network for intelligent diagnosis of machinery faults with similar features. IEEJ Trans Elec Electron Eng, 2019, 14: 1851–1864
Cao Y, Sun Y K, Xie G, et al. Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy. IEEE Trans Veh Technol, 2019, 68: 7544–7551
Zhang S, Dong Y, Ouyang Y, et al. Adaptive neural control for robotic manipulators with output constraints and uncertainties. IEEE Trans Neural Netw Learn Syst, 2018, 29: 5554–5564
He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2018, 29: 1174–1186
Xue Z B, Liu J C, Wu Z X, et al. Development and path planning of a novel unmanned surface vehicle system and its application to exploitation of Qarhan Salt Lake. Sci China Inf Sci, 2019, 62: 084202
Thiemann C, Treiber M, Kesting A. Estimating acceleration and lane-changing dynamics based on NGSIM trajectory Data. Transport Res Record J Transport Res Board, 2008, 2088: 90–101
Li P, Dargaville R, Cao Y, et al. Storage aided system property enhancing and hybrid robust smoothing for large-scale PV systems. IEEE Trans Smart Grid, 2017, 8: 2871–2879
Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computat, 2006, 18: 1527–1554
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86: 2278–2324
Chan T A, Hermeking H, Lengauer C, et al. 14-3-3σ is required to prevent mitotic catastrophe after DNA damage. Nature, 1999, 401: 616–620
Gers F A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Computat, 2000, 12: 2451–2471
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computat, 1997, 9: 1735–1780
Wang Y X, Liu J Q, Misic J, et al. Assessing optimizer impact on DNN model sensitivity to adversarial examples. IEEE Access, 2019, 7: 152766–152776
Acknowledgements
This work was supported by National Key R&D Program of China (Grant No. 2018YFB1201500), National Natural Science Foundation of China (Grant Nos. 61873201, 61773313, U1734210), Key Research and Development Program of Shaanxi Province (Grant No. 2018GY-139), Natural Science Foundation of Shaanxi Provincial Department of Education (Grant No. 19JS051), CERNET Innovation Project (Grant No. NGII20161201), and Scientific and Technological Planning Project of Beilin District of Xi’an (Grant No. GX1819).
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Xie, G., Shangguan, A., Fei, R. et al. Motion trajectory prediction based on a CNN-LSTM sequential model. Sci. China Inf. Sci. 63, 212207 (2020). https://doi.org/10.1007/s11432-019-2761-y
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DOI: https://doi.org/10.1007/s11432-019-2761-y