| UNDERSTANDING PEDESTRIANS’ CAR-HAILING INTENTION IN TRAFFIC SCENES
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| Zhenghao Wang , Jing Lian , Linhui Li , Yafu Zhou |
| Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology |
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| ABSTRACT |
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This study aims at the automatic understanding of pedestrians’ car-hailing intention in traffic scenes. Traffic
scenes are highly complex, with a completely random spatial distribution of pedestrians. Different pedestrians use different
behavior to express car-hailing intention, making it difficult to accurately understand the intention of pedestrians for
autonomous taxis in complex scenes. A novel intention recognition algorithm with interpretability is proposed in this paper to
solve the above problems. Firstly, we employ OpenPose to obtain skeleton data and the facial region. Then, we input the
facial region into a facial attention network to extract the facial attention features and infer whether the pedestrian is paying
attention to the ego-vehicle. In addition, the skeleton data are also input into a random forest classifier and GCN to extract
both explicit and implicit pose features. Finally, an interpretable fusion rule is proposed to fuse the facial and pose features.
The fusion algorithm can accurately and stably infer the pedestrians’ intention and identify pedestrians with car-hailing
intentions. In order to evaluate the performance of the proposed method, we collected road videos using experimental cars to
obtain suitable datasets, and established the corresponding evaluation benchmarks. The experimental results demonstrate that
the proposed algorithm has high accuracy and robustness. |
| Key Words:
Car-hailing intention, GCN, Random forest, Autonomous taxi
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Corresponding Author.
Linhui Li , Email. 
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