Motion Cue-Based Sudden Pedestrian Behavior Prediction Using Fuzzy Inference

As the motion of pedestrians is largely unpredictable, situational awareness presents a challenge for safe autonomous driving in urban areas. In particular, conventional sensor information about the dynamic states involved in determining and predicting pedestrian motion, including the walking speed, is significantly affected by latency when pedestrians suddenly increase their pace. In this paper, we propose a framework for predicting the steady-state walking speed of sudden pedestrian movement at the early stage of walking after heel-off. Based on the analysis that some motion cues during gait initiation are related to the steady-state walking speed, a fuzzy inference framework for predicting the steady-state walking speed, where the related motion cues are input to the inference model, is developed. The proposed framework can accurately predict the steady-state walking speed, even at the end of the first gait cycle. Moreover, the future trajectory of the pedestrian can be predicted using the piecewise linear speed model. Using the proposed framework, installed on the edge server of the cooperative-intelligent transportation system (C-ITS), this study aims to ensure the safety of autonomous vehicles by enabling them to successfully navigate the danger caused by sudden pedestrian movement. Experimental results obtained from testing the system at a real urban intersection verify the value offered by the proposed framework.