Uncertainty Differences in Computing Hierarchical Pedestrian Behaviors

Abstract

The development of autonomous vehicles presents significant challenges, particularly in predicting pedestrian behaviors. This study addresses the critical issue of uncertainty in such predictions by distinguishing between aleatoric (intrinsic randomness) and epistemic (knowledge limitations) uncertainties. Using evidential deep learning (EDL) techniques, we analyze these uncertainties in two key pedestrian behaviors: road crossing and short-term movement prediction. Our findings indicate that epistemic uncertainty is consistently higher than aleatoric uncertainty, highlighting the greater difficulty in predicting pedestrian actions due to limited information. Additionally, both types of uncertainties are more pronounced in crossing predictions compared to destination predictions, underscoring the complexity of future-oriented behaviors. These insights emphasize the necessity for AV algorithms to account for different levels of behavior-related uncertainties, ultimately enhancing the safety and efficiency of autonomous driving systems. This research contributes to a deeper understanding of pedestrian behavior prediction and lays the groundwork for future studies to explore scenario-specific uncertainty factors.