Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

doi:10.19682/j.cnki.1005-8885.2018.1025

中国邮电高校学报(英文版) ›› 2018, Vol. 25 ›› Issue (6): 31-43.doi: 10.19682/j.cnki.1005-8885.2018.1025

• Artificial Intelligence • 上一篇下一篇

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

Wang Likun, Liu Lu, Yu Dameng, Xu Qing, Wang Jianqiang

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

收稿日期:2018-08-07 修回日期:2018-12-31 出版日期:2018-12-30 发布日期:2019-02-26
通讯作者: Xu Qing, E-mail: qingxu@tsinghua.edu.cn E-mail:qingxu@tsinghua.edu.cn
作者简介:Xu Qing, E-mail: qingxu@tsinghua.edu.cn
基金资助:
This work was supported by the National Science Fund for Distinguished Young Scholars ( 51625503 ), the National Science Fund for Young Scholars (51605245), the National Natural Science Foundation of China, the Major Project (61790561) and Tsinghua-Honda Joint Project IV.

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

Wang Likun, Liu Lu, Yu Dameng, Xu Qing, Wang Jianqiang

State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

Received:2018-08-07 Revised:2018-12-31 Online:2018-12-30 Published:2019-02-26
Contact: Xu Qing, E-mail: qingxu@tsinghua.edu.cn E-mail:qingxu@tsinghua.edu.cn
About author:Xu Qing, E-mail: qingxu@tsinghua.edu.cn
Supported by:
This work was supported by the National Science Fund for Distinguished Young Scholars ( 51625503 ), the National Science Fund for Young Scholars (51605245), the National Natural Science Foundation of China, the Major Project (61790561) and Tsinghua-Honda Joint Project IV.

摘要/Abstract

摘要： Pedestrian trajectory prediction plays an important role in bothadvanced driving assistance system (ADAS) and autonomous vehicles. An algorithm for pedestrian trajectory prediction in crossing scenario is proposed. To obtain features of pedestrian motion, we develop a method for data labelling and pedestrian body orientation regression. Using the hierarchical features as domain of discourse, fuzzy logic rules are built to describe the transition between
different pedestrian states and motion models. With derived probability of each type of motion model we further predict the pedestrian trajectory in the next 1.5 s using switching Kalman filter (KF). The proposed algorithm is further verified in our dataset, and the result indicates that the proposed algorithm successfully predicts pedestrian' s crossing behavior 0.4 s earlier before pedestrian moves. Meanwhile, the precision of predicted trajectory surpasses
other methods including interacting multi-model KF and dynamic Bayesian network (DBN).

关键词: pedestrian protection, trajectory prediction, fuzzy logic, switching KF

Abstract: Pedestrian trajectory prediction plays an important role in bothadvanced driving assistance system (ADAS) and autonomous vehicles. An algorithm for pedestrian trajectory prediction in crossing scenario is proposed. To obtain features of pedestrian motion, we develop a method for data labelling and pedestrian body orientation regression. Using the hierarchical features as domain of discourse, fuzzy logic rules are built to describe the transition between
different pedestrian states and motion models. With derived probability of each type of motion model we further predict the pedestrian trajectory in the next 1.5 s using switching Kalman filter (KF). The proposed algorithm is further verified in our dataset, and the result indicates that the proposed algorithm successfully predicts pedestrian' s crossing behavior 0.4 s earlier before pedestrian moves. Meanwhile, the precision of predicted trajectory surpasses
other methods including interacting multi-model KF and dynamic Bayesian network (DBN).

Key words: pedestrian protection, trajectory prediction, fuzzy logic, switching KF

中图分类号:

TP31

Wang Likun, Liu Lu, Yu Dameng, Xu Qing, Wang Jianqiang. Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter[J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(6): 31-43.

参考文献

References
1. Schneider N, Gavrila D M. Pedestrian path prediction with recursive Bayesian filters: a comparative study. German Conference on Pattern Recognition, 2013: 174 -183
2. Goldhammer M, Gerhard M, Zernetsch S, et al. Early prediction of a pedestrian's trajectory at intersections. International IEEE Conference on Intelligent Transportation Systems, IEEE, 2014: 237 -242
3. Iryo-Asano M, Alhajyaseen W. Consideration of a pedestrian speed change model in the pedestrian-vehicle safety assessment of signalized crosswalks. Transportation Research Procedia, 2017, 21: 87 -97
4. Kooij J F P, Schneider N, Flohr F, et al. Context-based pedestrian path prediction. European Conference on Computer Vision. Springer, Cham, 2014: 618 -633
5. Karasev V, Ayvaci A, Heisele B, et al. Intent-aware long-term prediction of pedestrian motion. IEEE International Conference on Robotics and Automation, IEEE, 2016: 2543 -2549
6. Asahara A, Maruyama K, Sato A, et al. Pedestrian-movement prediction based on mixed Markov-chain model. ACM Sigspatial International Symposium on Advances in Geographic Information Systems, ACM-GIS 2011, November 01 -04, 2011, Chicago, Il, Usa, Proceedings, DBLP, 2011: 25 -33
7. Rehder E, Kloeden H. Goal-directed pedestrian prediction. IEEE International Conference on Computer Vision Workshop, IEEE, 2015: 139 -147
8. Kwak J Y, Lee E J, Ko B C, et al. Pedestrian's intention prediction based on fuzzy finite automata and spatial-temporal features. Electronic Imaging, 2016, 2016(3): 1 -6
9. Nasir M, Nahavandi S, Creighton D. Fuzzy simulation of pedestrian walking path considering local environmental stimuli. 2012, 19: 1 -6
10. Nasir M, Lim C P, Nahavandi S, et al. A genetic fuzzy system to model pedestrian walking path in a built environment. Simulation Modelling Practice and Theory, 2014, 45(6): 18 -34
11. Castro J L, Delgado M, Medina J, et al. An expert fuzzy system for predicting object collisions. Its application for avoiding pedestrian accidents. Expert Systems with Applications, 2011, 38(1): 486 -494
12. Keller C G, Hermes C, Gavrila D M. Will the pedestrian cross? Probabilistic path prediction based on learned motion features. 2011
13. Keller C G, Gavrila D M. Will the pedestrian cross? A study on pedestrian path prediction. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(2): 494 -506
14. Quintero R, Almeida J, Llorca D F, et al. Pedestrian path prediction using body language traits. Intelligent Vehicles Symposium Proceedings, IEEE, 2014: 317 -323
15. Chen Z, Ngai D C K, Yung N H C. Pedestrian behavior prediction based on motion patterns for vehicle-to-pedestrian collision avoidance. International IEEE Conference on Intelligent Transportation Systems, IEEE, 2008: 316 -321
16. Volz B, Mielenz H, Siegwart R, et al. Predicting pedestrian crossing using quantile regression forests. Intelligent Vehicles Symposium, IEEE, 2016: 426 -432
17. Goldhammer M, Kohler S, Doll K, et al. Camera based pedestrian path prediction by means of polynomial least-squares approximation and multilayer perceptron neural networks. Sai Intelligent Systems Conference, IEEE, 2016: 390 -399
18. Murphy K P. Switching Kalman filters, 1998

[1]	李文正贺鸣升. Imp Raft: a consensus algorithm based on Raft and storage compression consensus for IoT scenario[J]. 中国邮电高校学报(英文版), 2020, 27(3): 53-61.
[2]	费炜刘聪胡胜. Research on swarm intelligence optimization algorithm[J]. 中国邮电高校学报(英文版), 2020, 27(3): 1-20.
[3]	王红熳李晋忠邢颖周晓光. Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization[J]. 中国邮电高校学报(英文版), 2020, 27(1): 38-50.
[4]	Guan Hao, Rao Yongsheng, Xu Zhangtao. Design and implementation of web-based dynamic geometry drawing technology[J]. 中国邮电高校学报(英文版), 2019, 26(6): 43-53.
[5]	Han Xu, Wang Hongsu, Zhang Sanqian, Fu Qunchao, Liu Jun. Sentence segmentation for classical Chinese based on LSTM with radical embedding[J]. 中国邮电高校学报(英文版), 2019, 26(2): 1-8.
[6]	Sun Jiaze, Ling Beilei. Density PSO-based software module clustering algorithm[J]. 中国邮电高校学报(英文版), 2018, 25(4): 38-47.
[7]	张玲白中英 LU Yun-long ZHA Ya-xing LI Zhen-wen. Integrated intrusion detection model based on artificial immune[J]. Acta Metallurgica Sinica(English letters), 2014, 21(2): 83-90.
[8]	张毅何春江罗元陈凯邢武超. Improved perceptually non-uniform spectral compression for robust speech recognition [J]. Acta Metallurgica Sinica(English letters), 2013, 20(4): 122-126.
[9]	. Study on top-down estimation method of software project planning.[J]. Acta Metallurgica Sinica(English letters), 2006, 13(2): 108-111.
[10]	吕芙蓉; 吕廷杰. Inheritance Optimization of Extend Case Transfer Model of Interoganization Workflows Management[J]. Acta Metallurgica Sinica(English letters), 2006, 13(1): 99-103.

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

Pedestrian trajectory prediction in crossing scenario using fuzzy logic and switching Kalman filter

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价