Applying the Extended Theory of Planned Behavior to Pedestrian Intention Estimation

Haoran Wu, Sifa Zheng, Qing Xu, Jian-Qiang Wang

Abstract: Intelligent vehicles should be capable to understand the intention of other traffic participants when driving on urban roads. Yet, current approaches mostly emphasize the importance of the crossing/not-crossing (C/NC) problem and neglect the intention estimation task. To this end, we propose a pedestrian intention estimation method based on the extended theory of planned behavior (TPB). In contrast to previous qualitative modeling based on surveys and questionnaires, neural networks and hand-crafted rules are designed to quantitatively model the components of the extended TPB in the proposed architecture. Besides, the interaction between the components is simulated by a mixed classification strategy. Our pedestrian intention estimation model achieves 82% accuracy and outperforms the baseline method by 3% on the pedestrian intention estimation (PIE) dataset.