Predicting pedestrian road-crossing assertiveness for autonomous vehicle control

TY - GEN

T1 - Predicting pedestrian road-crossing assertiveness for autonomous vehicle control

AU - Camara, F.

AU - Giles, O.

AU - Madigan, R.

AU - Rothmuller, M.

AU - Rasmussen, P. Holm

AU - Vendelbo-Larsen, S. A.

AU - Markkula, G.

AU - Lee, Y. M.

AU - Garach, Laura

AU - Merat, N.

AU - Fox, C. W.

N1 - Publisher Copyright: © 2018 IEEE.

PY - 2018/12/7

Y1 - 2018/12/7

N2 - 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 - logistic 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.

AB - 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 - logistic 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.

KW - Agent-Human Interactions

KW - Autonomous Vehicles

KW - Human Factors

UR - http://www.scopus.com/inward/record.url?scp=85060440634&partnerID=8YFLogxK

U2 - 10.1109/ITSC.2018.8569282

DO - 10.1109/ITSC.2018.8569282

M3 - Conference contribution

AN - SCOPUS:85060440634

T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

SP - 2098

EP - 2103

BT - 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018

Y2 - 4 November 2018 through 7 November 2018

ER -