Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

Ting Zhang; Wenjie Song; Mengyin Fu; Yi Yang; Meiling Wang

doi:10.1109/JAS.2021.1003952

Volume 8 Issue 10

Oct. 2021

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(10): 1657-1666

T. Zhang, W. J. Song, M. Y. Fu, Y. Yang, and M. L. Wang, "Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657-1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952

Citation:

T. Zhang, W. J. Song, M. Y. Fu, Y. Yang, and M. L. Wang, "Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model," IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657-1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952

Citation:

PDF( 8235 KB)

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

doi: 10.1109/JAS.2021.1003952

Ting Zhang^{1, 2
,},
Wenjie Song^{1, 2
,
,},
Mengyin Fu^{1, 2
,},
Yi Yang^{1, 2
,},
Meiling Wang^{1, 2
,}

1.
State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China
2.
Nanjing University of Science and Technology,Nanjing 210014, China

Funds: This work was partly supported by the National Natural Science Foundation of China (61903034, U1913203, 61973034, 91120003), the Program for Changjiang Scholars and Innovative Research Team in University (IRT-16R06, T2014224), China Postdoctoral Science Foundation funded project (2019TQ0035), Beijing Institute of Technology Research Fund Program for Young Scholars

More Information

Author Bio:
Ting Zhang received the B.S. degree in automation from Beijing Institute of Technology, China in 2018, where she received the Doctor recommendation qualification. She is currently a Ph.D. candidate for the third year in navigation guidance and control at the School of Automation, Beijing Institute of Technology, China. Her research interests include trajectories and behavioral predictions of unmanned vehicles in a variety of traffic environments, as well as prediction-based decision making and planning

Wenjie Song received the B.S. and Ph.D. degrees from Beijing Institute of Technology, China, in 2013 and 2019, respectively. He studied in Princeton University as a Visiting Scholar from 2016 to 2017. He is currently an Assistant Professor with the School of Automation, Beijing Institute of Technology, Beijing, China. His research interests include autonomous driving, environmental perception, SLAM, path planning, etc. He has taken part in “China Intelligent Vehicle Future Challenge” for several times as the captain or the core member

Mengyin Fu received the Ph.D. degree from Chinese Academy of Sciences, China. He is the President of Nanjing University of Science and Technology, China. His research interest covers integrated navigation, intelligent navigation, image processing, learning and recognition as well as their applications. He was elected as the Yangtze River Scholar Distinguished Professor in 2009, and won the Guanghua Engineering Science and Technology Award for Youth Award in 2010. He has got National Science and Technology Progress Award for several times in recent years

Yi Yang received the Ph.D. degree in automation from Beijing Institute of Technology, Beijing, China, in 2010. He is currently a Professor with the School of Automation, Beijing Institute of Technology, Beijing, China. His research interests include autonomous vehicles, bioinspired robots, intelligent navigation, semantic mapping, scene understanding, motion planning and control, and robot design and development. He is author/co-author of more than 50 conference and journal papers in the area of unmanned ground vehicle

Meiling Wang received the B.S. degree in automation from the Beijing Institute of Technology, China, in 1992, and the M.S. and Ph.D. degrees from Beijing Institute of Technology, China, in 1995 and 2007, respectively. She has been teaching in Beijing Institute of Technology since 1995, and worked in University of California San Diego as a Visiting Scholar in 2004. She is currently the Director of Integrated Navigation and Intelligent Navigation Laboratory, Beijing Institute of Technology, China. Her research interests include advanced technology of sensing and detecting and vehicle intelligent navigation. She was elected as the Yangtze River scholar Distinguished Professor in 2014
Corresponding author: Wenjie Song, e-mail: songwj@bit.edu.cn
Received Date: 2020-10-26
Revised Date: 2020-12-10
Accepted Date: 2021-01-10

Available Online: 2023-05-11

Abstract

Abstract

Intersections are quite important and complex traffic scenarios, where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles. Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined, this paper proposes a long short-term memory based (LSTM-based) framework that combines intention prediction and trajectory prediction together. First, we build an intersection prior trajectories model (IPTM) by clustering and statistically analyzing a large number of prior traffic flow trajectories. The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory, and also serves as a reference for credibility evaluation. Second, we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage. Furthermore, the predicted intention is also a key that is associated with the prior trajectories model. The proposed framework is validated on two publically released datasets, next generation simulation (NGSIM) and INTERACTION. Compared with other prediction methods, our framework is able to sample a trajectory from the estimated distribution, with its accuracy improved by about 20%. Finally, the credibility evaluation, which is based on the prior trajectories model, makes the framework more practical in the real-world applications.
- Autonomous vehicle,
- intersection,
- motion prediction,
- prior trajectories model,
- turning intention

FullText(HTML)

References(30)

References

[1]	E. H. Choi, “Crash factors in intersection-related crashes: An on-scene perspective,” National Center for Statistics and Analysis, Washington, USA, HS-811 366, 2010. [Online]. Available: https://trid.trb.org/view/1083638.
[2]	C. Lienke, C. Wissing, M. Keller, T. Nattermann, and T. Bertram, “Predictive driving: Fusing prediction and planning for automated highway driving,” IEEE Trans. Intell. Veh., vol. 4, no. 3, pp. 456–467, Sep. 2019. doi: 10.1109/TIV.2019.2919477
[3]	Y. P. Hu, W. Zhan, and M. Tomizuka, “Probabilistic prediction of vehicle semantic intention and motion,” in Proc. IEEE Intelligent Vehicles Symp., Changshu, China, 2018, pp. 307–313.
[4]	A. Zyner, S. Worrall, and E. Nebot, “Naturalistic driver intention and path prediction using recurrent neural networks,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 4, pp. 1584–1594, Apr. 2020. doi: 10.1109/TITS.2019.2913166
[5]	N. Deo and M. M. Trivedi, “Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs,” in Proc. IEEE Intelligent Vehicles Symposium, Changshu, China, 2018, pp. 1179–1184.
[6]	B. Tang, S. Khokhar, and R. Gupta, “Turn prediction at generalized intersections,” in Proc. IEEE Intelligent Vehicles Symposium, Seoul, South Korea, 2015, pp. 1399–1404.
[7]	D. J. Phillips, T. A. Wheeler, and M. J. Kochenderfer, “Generalizable intention prediction of human drivers at intersections,” in Proc. IEEE Intelligent Vehicles Symposium, Los Angeles, USA, 2017, pp. 1665–1670.
[8]	A. Zyner, S. Worrall, and E. Nebot, “A recurrent neural network solution for predicting driver intention at unsignalized intersections,” IEEE Robot. Autom. Lett., vol. 3, no. 3, pp. 1759–1764, Jul. 2018. doi: 10.1109/LRA.2018.2805314
[9]	F. Poggenhans, J. H. Pauls, J. Janosovits, S. Orf, M. Naumann, F. Kuhnt, and M. Mayr, “Lanelet2: A high-definition map framework for the future of automated driving,” in Proc. 21st Int. Conf. on Intelligent Transportation Systems, Maui, USA, 2018, pp. 1672–1679.
[10]	P. Kumar, M. Perrollaz, S. Lefèvre, and C. Laugier, “Learning-based approach for online lane change intention prediction,” in Proc. IEEE Intelligent Vehicles Symposium, Gold Coast, Australia, 2013, pp. 797–802.
[11]	M. Schreier, V. Willert, and J. Adamy, “Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems,” in Proc. 17th Int. IEEE Conf. on Intelligent Transportation Systems, Qingdao, China, 2014, pp. 334–341.
[12]	S. Yoon and D. Kum, “The multilayer perceptron approach to lateral motion prediction of surrounding vehicles for autonomous vehicles,” in Proc. IEEE Intelligent Vehicles Symposium, Gothenburg, Sweden, 2016, pp. 1307–1312.
[13]	G. T. Xie, H. B. Gao, L. J. Qian, B. Huang, K. Q. Li, and J. Q. Wang, “Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models,” IEEE Trans. Ind. Electron., vol. 65, no. 7, pp. 5999–6008, Jul. 2018. doi: 10.1109/TIE.2017.2782236
[14]	A. Wolfermann, W. K. M. Alhajyaseen, and H. Nakamura, “Modeling speed profiles of turning vehicles at signalized intersections,” in Proc. 3rd Int. Conf. on Road Safety and Simulation, Indianapolis Indiana, USA, 2011.
[15]	A. Kawasaki and T. Tasaki, “Trajectory prediction of turning vehicles based on intersection geometry and observed velocities,” in Proc. IEEE Intelligent Vehicles Symposium, Changshu, China, 2018, pp. 511–516.
[16]	D. Roy, T. Ishizaka, C. K. Mohan, and A. Fukuda, “Vehicle trajectory prediction at intersections using interaction based generative adversarial networks,” in Proc. IEEE Intelligent Transportation Systems Conf., Auckland, New Zealand, 2019, pp. 2318–2323.
[17]	H. B. Ma, J. C. Li, W. Zhan, and M. Tomizuka, “Wasserstein generative learning with kinematic constraints for probabilistic interactive driving behavior prediction,” in Proc. IEEE Intelligent Vehicles Symposium, Paris, France, 2019, pp. 2477–2483.
[18]	R. Chandra, U. Bhattacharya, A. Bera, and D. Manocha, “Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 8475–8484.
[19]	T. Y. Zhao, Y. F. Xu, M. Monfort, W. Choi, C. Baker, Y. B. Zhao, Y. Z. Wang, and Y. N. Wu, “Multi-agent tensor fusion for contextual trajectory prediction,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition, Long Beach, USA, 2019, pp. 12118–12126.
[20]	X. Li, X. W. Ying, and M. C. Chuah, “GRIP: Graph-based interaction-aware trajectory prediction,” in Proc. IEEE Intelligent Transportation Systems Conf., Auckland, New Zealand, 2019, pp. 3960–3966.
[21]	R. Chandra, T. R. Guan, S. Panuganti, T. Mittal, U. Bhattacharya, A. Bera, and D. Manocha, “Forecasting trajectory and behavior of road-agents using spectral clustering in graph-LSTMs,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4882–4890, Jul. 2020. doi: 10.1109/LRA.2020.3004794
[22]	J. C. Pan, H. Y. Sun, K. C. Xu, Y. F. Jiang, X. Q. Xiao, J. T. Hu, and J. H. Miao, “Lane attention: Predicting vehicles’ moving trajectories by learning their attention over lanes,” arXiv preprint arXiv: 1909.13377, 2019.
[23]	N. Deo and M. M. Trivedi, “Convolutional social pooling for vehicle trajectory prediction,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition Workshops, Salt Lake City, USA, 2018, pp. 1549–15498.
[24]	M. P. Dubuisson and A. K. Jain, “A modified Hausdorff distance for object matching,” in Proc. 12th Int Conf. Pattern Recognition, Jerusalem, Israel, 1994, pp. 566–568.
[25]	S. Atev, G. Miller, and N. P. Papanikolopoulos, “Clustering of vehicle trajectories,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 647–657, Sep. 2010. doi: 10.1109/TITS.2010.2048101
[26]	L. Xin, P. Wang, C. Y. Chan, J. Y. Chen, S. E. Li, and B. Cheng, “Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks,” in Proc. 21st Int. Conf. Intelligent Transportation Systems, Maui, USA, 2018, pp. 1441–1446.
[27]	J. Halkias and J. Colyar, “Ngsim overview. [Online]. Available: https://www.fhwa.dot.gov/publications/research/operations/its/06135/06135.pdf. Accessed on: July 26, 2020.
[28]	B. Coifman and L. Z. Li, “A critical evaluation of the next generation simulation (NGSIM) vehicle trajectory dataset,” Transp. Res. B Methodol., vol. 105, pp. 362–377, Nov. 2017. doi: 10.1016/j.trb.2017.09.018
[29]	R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems,” in Proc. 21st Int Conf. Intelligent Transportation Systems, Maui, USA, 2018, pp. 2118–2125.
[30]	W. Zhan, L. T. Sun, D. Wang, H. J. Shi, A. Clausse, M. Naumann, J. Kummerle, H. Konigshof, C. Stiller, A. de La Fortelle, and M. Tomizuka, “INTERACTION dataset: An Int., adversarial and cooperative moTION dataset in interactive driving scenarios with semantic maps,” arXiv preprint arXiv: 1910.03088, 2019.

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(6) / Tables(3)

Get Citation

PDF

XML

Article Metrics

Article views (1776) PDF downloads(122)

Highlights

Framework: We combine intention prediction and trajectory prediction for the specified intersection scenarios. The predicted intention is not only a one-dimensional feature for trajectory prediction, but also a part of the key directly related to a prior trajectory boundary.
Distribution Parameter Constraints: In order to generate the trajectory boundary in the intersection, we propose an intersection prior trajectories model (IPTM) to create statistics of the historical trajectories, which approximates the distribution of the ground truth.
Evaluation Metrics: We analyse the credibility of the estimated trajectory by applying the modified Hausdorff distance criteria to the predicted trajectory and the prior trajectory distribution, which does not require the ground truth. The prediction that conforms to the prior trajectory distribution seems more reasonable.

Vehicle Motion Prediction at Intersections Based on the Turning Intention and Prior Trajectories Model

doi: 10.1109/JAS.2021.1003952

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content