Abstract

Autonomous vehicles (AVs) must interact with other
road users including pedestrians. Unlike passive environments,
pedestrians are active agents having their own utilities and
decisions, which must be inferred and predicted by AVs in order
to control interactions with them and navigation around them.
In particular, when a pedestrian wishes to cross the road in
front of the vehicle at an unmarked crossing, the pedestrian
and AV must compete for the space, which may be considered
as a game-theoretic interaction in which one agent must yield
to the other. To inform AV controllers in this setting, this study
collects and analyses data from real-world human road crossings
to determine what features of crossing behaviours are predictive
about the level of assertiveness of pedestrians and of the eventual
winner of the interactions. It presents the largest and most
detailed data set of its kind known to us, and new methods to
analyze and predict pedestrian-vehicle interactions based upon
it. Pedestrian-vehicle interactions are decomposed into sequences
of independent discrete events. We use probabilistic methods –
regression and decision tree regression – and sequence analysis
to analyze sets and sub-sequences of actions used by both
pedestrians and human drivers while crossing at an intersection,
to find common patterns of behaviour and to predict the winner
of each interaction. We report on the particular features found
to be predictive and which can thus be integrated into game-
theoretic AV controllers to inform real-time interactions.