Pedestrian Intention Prediction for Autonomous Vehicles: A Comprehensive Survey

Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge social, economic and environmental benefits. However, the rising safety apprehensions for vulnerable road users (VRU) alongside have become a stumbling block to the large-scale implementation of AVs. Deciphering intention in advance considering all social norms and context surrounding the VRU is a highly challenging task due to the huge amount of variability in their motion, actions and end goals. Based on this pensiveness, this paper extensively surveys the variety of techniques applied to anticipate pedestrian intention and classifies them from multiple perspectives. Some newly introduced datasets with added complexities of human behaviour on road have also been outlined. It also provides a comparative analysis of the performance of pedestrian intention prediction approaches on several benchmark datasets as per the various assessment parameters available. In addition to this, several potential challenges and their possible solutions paving way for future research initiatives have also been thoroughly analysed in this endeavour.