TY - JOUR

T1 - Uncertainty-aware Pedestrian Crossing Prediction via Reinforcement Learning

AU - Dai, Siyang

AU - Liu, Jun

AU - Cheung, Ngai-Man

PY - 2024/10/31

Y1 - 2024/10/31

N2 - Pedestrian safety is a huge concern for deploying autonomous vehicles in urban environments. Accidents involving pedestrians pose a higher degree of severity, sometimes causing serious injuries and fatalities [1]. It’s a challenging task to predict whether a pedestrian will cross the road since they can move in any direction and change motion suddenly. The inherent uncertainty in pedestrian motion has been addressed with probabilistic models in previous works. However, these models are too computationally expensive for real-time predictions. In this paper, we propose a novel reinforcement learning (RL) framework which produces soft labels for the training dataset in order to address the observed data uncertainty. We formulate novel state representations incorporating predictive uncertainty to learn more informative soft labels that improve the model performance and reliability. Finally, we validate the proof of concept with two benchmark datasets and show with extensive experiments on competitive prediction models that our method (even using fewer input modalities) significantly improves the accuracy and f1 score by up to 12% and 13% respectively. We also show that soft labeling as a form of regularization increases model reliability where the model is more accurate when the confidence level is high and more aware of its limitations with indication of low confidence.

AB - Pedestrian safety is a huge concern for deploying autonomous vehicles in urban environments. Accidents involving pedestrians pose a higher degree of severity, sometimes causing serious injuries and fatalities [1]. It’s a challenging task to predict whether a pedestrian will cross the road since they can move in any direction and change motion suddenly. The inherent uncertainty in pedestrian motion has been addressed with probabilistic models in previous works. However, these models are too computationally expensive for real-time predictions. In this paper, we propose a novel reinforcement learning (RL) framework which produces soft labels for the training dataset in order to address the observed data uncertainty. We formulate novel state representations incorporating predictive uncertainty to learn more informative soft labels that improve the model performance and reliability. Finally, we validate the proof of concept with two benchmark datasets and show with extensive experiments on competitive prediction models that our method (even using fewer input modalities) significantly improves the accuracy and f1 score by up to 12% and 13% respectively. We also show that soft labeling as a form of regularization increases model reliability where the model is more accurate when the confidence level is high and more aware of its limitations with indication of low confidence.

U2 - 10.1109/TCSVT.2024.3400391

DO - 10.1109/TCSVT.2024.3400391

M3 - Journal article

VL - 34

SP - 9540

EP - 9549

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

SN - 1051-8215

IS - 10

ER -