中国邮电高校学报(英文) ›› 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   

  1. 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   

  1. 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: 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: