Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

  • Xuchuan Li Chang'an University
  • Lingkun Fan Chang’an University
  • Tao Chen Chang’an University
  • Shuaicong Guo Chang’an University
Keywords: trajectory prediction, external parameters, dynamic sensitive area, long-short term memory


The ability to predict the motion of vehicles is essential for autonomous vehicles. Aiming at the problem that existing models cannot make full use of the external parameters including the outline of vehicles and the lane, we proposed a model to use the external parameters thoroughly when predicting the trajectory in the straight-line and non-free flow state. Meanwhile, dynamic sensitive area is proposed to filter out inconsequential surrounding vehicles. The historical trajectory of the vehicles and their external parameters are used as inputs. A shared Long Short-Term Memory (LSTM) cell is proposed to encode the explicit states obtained by mapping historical trajectory and external parameters. The hidden states of vehicles obtained from the last step are used to extract latent driving intent. Then, a convolution layer is designed to fuse hidden states to feed into the next prediction circle and a decoder is used to decode the hidden states of the vehicles to predict trajectory. The experiment result shows that the dynamic sensitive area can shorten the training time to 75.86% of the state-of-the-art work. Compared with other models, the accuracy of our model is improved by 23.7%. Meanwhile, the model's ability of anti-interference of external parameters is also improved.


Deo N, Trivedi MM. Convolutional social pooling for vehicle trajectory prediction. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. 2018;1805: 1468-76. DOI: 10.1109/CVPRW.2018.00196

Ji X, et al. Driving intention recognition and vehicle trajectory prediction based on LSTM network. Journal of China Highway. 2019;32(6): 34-42.

Barth A, Franke U. Where will the oncoming vehicle be the next second? 2008 IEEE Intelligent Vehicles Symposium, 4-6 June 2008, Eindhoven, The Netherlands; 2018. p. 1068-1073. DOI: 10.1109/IVS.2008.4621210

Toledo-Moreo R, Zamora-Izquierdo MA. IMM-based lane-change prediction in highways with low-cost GPS/INS. IEEE Transactions on Intelligent Transportation Systems. 2009;10(1): 180-5. DOI: 10.1109/TITS.2008.2011691

Schubert R, et al. Empirical evaluation of vehicular models for ego motion estimation. 2011 IEEE Intelligent Vehicles Symposium (IV), 5-9 June 2011, Baden-Baden, Germany; 2011. p. 534-539. DOI: 10.1109/IVS.2011.5940526

Houenou A, Bonnifait P, Cherfaoui V, Yao W. Vehicle trajectory prediction based on motion model and maneuver recognition. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3-7 Nov. 2013, Tokyo, Japan; 2013. p. 4363-4369. DOI: 10.1109/IROS.2013.6696982

Singh K, Li B. Estimation of traffic densities for multilane roadways using a Markov model approach. IEEE Transactions on Industrial Electronics. 2012;59(11): 4369-76. DOI: 10.1109/TIE.2011.2180271

Berndt H, Emmert J, Dietmayer K. Continuous driver intention recognition with hidden Markov models. 2008 11th International Conference on Intelligent Transportation Systems, 12-15 Oct. 2008, Beijing, China; 2008. p. 1189-1194. DOI: 10.1109/ITSC.2008.4732630

Huang L, Guo H, Zhang R, Wu J. Lane-changing behavior model of unmanned vehicles based on LSTM in human-machine hybrid driving environment. Journal of China Highway. 2020;33(07): 156-66.

Tang J, et al. Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications. 2018;91: 452-63. DOI: 10.1016/j.eswa.2017.09.025

Wissing C, Nattermann T, Glander K, Bertram T. Probabilistic time-to-lane-change prediction on highways. 2017 IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA, USA; 2017. p. 1452-1457. DOI: 10.1109/IVS.2017.7995914

Zyner A, Worrall S, Ward J, Nebot E. Long short term memory for driver intent prediction. 2017 IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA, USA; 2017. p. 1484-1489. DOI: 10.1109/IVS.2017.7995919

Phillips DJ, Wheeler TA, Kochenderfer MJ. Generalizable intention prediction of human drivers at intersections. 2017 IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA, USA; 2017. p. 1665-1670. DOI: 10.1109/IVS.2017.7995948

Kuefler A, Morton J, Wheeler TA, Kochenderfer MJ. Imitating driver behavior with generative adversarial networks. 2017 IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA, USA; 2017. p. 204-211. DOI: 10.1109/IVS.2017.7995721

Liu Z, Wu X, Ni J, Zhang T. Driving intention recognition based on the cascade algorithm of HMM and SVM. Automotive Engineering. 2018;40(07): 858-64.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8): 1735-80. DOI: 10.1162/neco.1997.9.8.1735

Alahi A, et al. Social LSTM: Human trajectory prediction in crowded spaces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27-30 June 2016, Las Vegas, NV, USA; 2016. p. 961-971. DOI: 10.1109/CVPR.2016.110

Liu C, Liang J. Vehicle trajectory prediction based on attention mechanism. Journal of Zhejiang University (Engineering Science Edition). 2019;14: 1-8.

Wang Y, Yang S, Pan B. Research on vehicle trajectory of highway straight section. Highway Traffic Technology. 2016;33(02): 111-9.

Spacek P. Track behavior in curve areas: Attempt at typology. Journal of Transportation Engineering. 2005;131: 669-76. DOI: 10.1061/(ASCE)0733-947X(2005)131:9(669)

Institution CS. GB/T 51328-2018. Stand for urban comprehensive transport system planning. Ministry of Housing and Urban-Rural Development, PRC; 2003. p. 33-41.

NGSIM: Next generation simulation. In: DataHub I; 2016.

Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. Neural Information Processing Systems. 2015: 91-9.

How to Cite
Li X, Fan L, Chen T, Guo S. Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters. Promet - Traffic&Transportation. 2021;33(5):745-54. DOI: 10.7307/ptt.v33i5.3718