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Efficient Pedestrian Crossing Intention Anticipation Based on Action-Conditioned Interaction
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Biao Yang1, Zhiwen Wei1, Rongrong Ni1, Hai Wang2, Yingfeng Cai3, Changchun Yang1
Automotive Engineering | 2024, 46(1) : 29 - 38
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Automotive Engineering | 2024, 46(1): 29-38
Feature Topic: Intelligent Cockpit and Human-Machine Interaction
Efficient Pedestrian Crossing Intention Anticipation Based on Action-Conditioned Interaction
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Biao Yang1, Zhiwen Wei1, Rongrong Ni1, Hai Wang2, Yingfeng Cai3, Changchun Yang1
Affiliations
  • 1 School of Microelectronics and Control Engineering,Changzhou University,Changzhou  213159
  • 2 School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang  212013
  • 3 Institute of Automotive Engineering,Jiangsu University,Zhenjiang  212013
Published: 2024-01-25 doi: 10.19562/j.chinasae.qcgc.2024.01.004
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With acceleration of the urbanization process, pedestrianvehicle conflicts have become a significant issue that modern society urgently needs to solve. In complex traffic scenarios, pedestrian crossing behavior leads to frequent traffic accidents. Accurately and timely anticipating pedestrian crossing intentions is crucial for avoiding pedestrianvehicle conflicts, improving driving safety, and ensuring pedestrian safety. An Efficient ActionConditioned Interaction Pedestrian Crossing Intention Anticipation Framework (EAIPF) is proposed in this paper to anticipate pedestrian crossing intention. EAIPF introduces in a pedestrian action encoding module to enhance the representation ability of multimodal action patterns and discover deep skeletal context information. At the same time, the scene object interaction module is introduced to explore interaction information with objects and understand advanced semantic clues in traffic scenes. Finally, the intention anticipation module fuses pedestrian action and object interaction features to achieve robust anticipation of pedestrian crossing intentions. The proposed method is verified on two public datasets, JAAD and PIE, achieving the accuracy of 89% and 90%, respectively, indicating that the proposed method can accurately anticipate pedestrian crossing intentions in complex traffic scenarios.

pedestrian-vehicle conflict  /  crossing intention anticipation  /  graph convolution network  /  pedestrian action encoding  /  scene understanding
Biao Yang, Zhiwen Wei, Rongrong Ni, Hai Wang, Yingfeng Cai, Changchun Yang. Efficient Pedestrian Crossing Intention Anticipation Based on Action-Conditioned Interaction[J]. Automotive Engineering, 2024 , 46 (1) : 29 -38 . DOI: 10.19562/j.chinasae.qcgc.2024.01.004
Year 2024 volume 46 Issue 1
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.01.004
  • Receive Date:2023-06-04
  • Online Date:2025-07-20
  • Published:2024-01-25
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  • Received:2023-06-04
  • Revised:2023-07-03
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Affiliations
    1 School of Microelectronics and Control Engineering,Changzhou University,Changzhou  213159
    2 School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang  212013
    3 Institute of Automotive Engineering,Jiangsu University,Zhenjiang  212013
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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