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

doi:10.19682/j.cnki.1005-8885.2018.1025

JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2018, Vol. 25 ›› Issue (6): 31-43.doi: 10.19682/j.cnki.1005-8885.2018.1025

• Artificial intelligence • Previous Articles Next Articles

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.

Abstract

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

CLC Number:

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.

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

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