Abstract
This study alms 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.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (52172382, 61976039) and the China Fundamental Research Funds for the Central Universities (DUT20GJ207), and Science and Technology Innovation Fund of Dalian (2021JJ12GX015).
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Wang, Z., Lian, J., Li, L. et al. Understanding Pedestrians’ Car-Hailing Intention in Traffic Scenes. Int.J Automot. Technol. 23, 1023–1034 (2022). https://doi.org/10.1007/s12239-022-0089-8
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DOI: https://doi.org/10.1007/s12239-022-0089-8