Pedestrian Models for Autonomous Driving Part II: high level models of human behavior

Abstract

Autonomous vehicles (AVs) must share space with human pedestrians, both in on-road cases such as cars at pedestrian crossings and off-road cases such as delivery vehicles navigating through crowds on highstreets. Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detection and tracking which enable such modelling. This narrative review article is Part II of a pair which together survey the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians likely destinations and paths, to game theoretic models of interactions between pedestrians and autonomous vehicles. It finds that there remain many gaps in the literature at these higher Levels required for fully autonomous driving. At these levels, early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative AV control algorithms.