Citation

Yang B, Zhan W, Wang P, Chan C, Cai Y, Wang N. IEEE Trans. Intel. Transp. Syst. 2022; 23(6): 5338-5349.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2021.3053031

PMID

unavailable

Abstract

The booming self-driving cars need to understand the behaviors of other road users for better performance. Recognizing pedestrians' crossing intentions is one of the most critical capabilities of self-driving cars to guarantee safe operations in the urban environment. Some researchers try to predict the future trajectories of pedestrians to avoid a potential collision. Others extract skeleton features from pedestrians to detect specific actions related to the crossing intention. All these methods either neglect the abundant appearance information of pedestrians, or work poorly under severe conditions, e.g., pedestrians standing far away, dim light, and occlusions. In this work, a pedestrian crossing intention recognition (PCIR) framework is proposed to recognize pedestrians' crossing intention. A module for searching targets of interest is introduced to find the pedestrians who are likely to cross the streets, and perform scene perception simultaneously. An action recognition module utilizes a 3D convolutional neural network (CNN) to automatically extract spatiotemporal features that imply early actions before crossing, like limb movements. A distance encoding module makes full use of the contextual cues, e.g., distances between pedestrians and the oncoming vehicle, local traffic scenes around pedestrians, and vehicle speeds, to improve the recognition accuracy obtained from the action recognition module only. Finally, the PCIR framework fuses these factors to predict pedestrians' crossing intentions by minimizing a focal loss. Experimental evaluations verify the effectiveness of the proposed method in recognizing pedestrians' crossing intentions. Comparisons with the skeleton-based methods reveal the robustness of the PCIR framework in the urban environment.

Language: en

Keywords

Automobiles; Trajectory; Feature extraction; Three-dimensional displays; Spatiotemporal phenomena; 3D convolutional neural network; action recognition; Autonomous automobiles; contextual information; Crossing intention; self-driving car; Skeleton