A Literature Review on the Prediction of Pedestrian Behavior in Urban Scenarios

The ability to anticipate pedestrian actions on streets is a safety issue for intelligent cars and has increasingly drawn the attention of the automotive industry. Estimating when pedestrians will cross streets has proved a challenging task, since they can move in many different directions, suddenly change motion, be occluded by a variety of obstacles and distracted while talking to other pedestrians or typing on a mobile phone. Moreover, their decisions can also be affected by several factors. This paper explores the ways pedestrians' intention estimation has been studied, evaluated, and evolved. It provides a literature review on pedestrian behavior prediction, addresses available solutions, state-of-the-art developments, and hurdles to be overcome towards reaching a solution that is closer to the human ability to predict and interpret such scenarios. Although many studies can precisely estimate pedestrians' positioning one second before they cross a street, most of them cannot precisely predict when they will stop at a curb.