Predicting pedestrian road-crossing assertiveness for autonomous vehicle control

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.

[1]  Albert N. Shiryaev,et al.  Optimal Stopping Rules , 1980, International Encyclopedia of Statistical Science.

[2]  Wendy Ju,et al.  Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles , 2016, 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[3]  Irene Gohl,et al.  Analyzing driver-pedestrian interaction at crosswalks: A contribution to autonomous driving in urban environments , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[4]  Natasha Merat,et al.  Empirical game theory of pedestrian interaction for autonomous vehicles , 2018 .

[5]  Juan José Burred,et al.  Genetic motif discovery applied to audio analysis , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[6]  Stefania Bandini,et al.  Towards Modelling Pedestrian-Vehicle Interactions: Empirical Study on Urban Unsignalized Intersection , 2016, ArXiv.

[7]  John K. Tsotsos,et al.  Agreeing to cross: How drivers and pedestrians communicate , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[8]  Shrikanth V. Mamidipalli A Review of Analysis Techniques and Data Collection Methods for Modeling Pedestrian Crossing Behaviors , 2015 .

[9]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[10]  Jacques M. B. Terken,et al.  Pedestrian Interaction with Vehicles: Roles of Explicit and Implicit Communication , 2017, AutomotiveUI.

[11]  Bartek Wilczynski,et al.  Biopython: freely available Python tools for computational molecular biology and bioinformatics , 2009, Bioinform..

[12]  P. D’haeseleer How does DNA sequence motif discovery work? , 2006, Nature Biotechnology.

[13]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[14]  Natasha Merat,et al.  When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions , 2018, VEHITS.

[15]  Alʹbert Nikolaevich Shiri︠a︡ev,et al.  Optimal stopping rules , 1977 .

[16]  Guangquan Lu,et al.  Modeling Crossing Behavior of Drivers at Unsignalized Intersections with Consideration of Risk Perception , 2017 .

[17]  Satish V. Ukkusuri,et al.  Modeling of Motorist-Pedestrian Interaction at Uncontrolled Mid-block Crosswalks , 2003 .

[18]  Nicolas Guéguen,et al.  A pedestrian’s stare and drivers’ stopping behavior: A field experiment at the pedestrian crossing , 2015 .

[19]  John K. Tsotsos,et al.  Understanding Pedestrian Behavior in Complex Traffic Scenes , 2018, IEEE Transactions on Intelligent Vehicles.

[20]  Ananthan K. Pillai,et al.  Virtual Reality based Study to Analyse Pedestrian attitude towards Autonomous Vehicles , 2017 .

[21]  George Yannis,et al.  Pedestrian Risk Taking While Road Crossing: A Comparison of Observed and Declared Behaviour , 2016 .

[22]  Shinpei Kato,et al.  An Open Approach to Autonomous Vehicles , 2015, IEEE Micro.

[23]  Natasha Merat,et al.  Filtration analysis of pedestrian-vehicle interactions for autonomous vehicle control , 2018 .

[24]  Dariu Gavrila,et al.  Context-Based Pedestrian Path Prediction , 2014, ECCV.

[25]  Malte Risto,et al.  Human-Vehicle Interfaces: The Power of Vehicle Movement Gestures in Human Road User Coordination , 2017 .

[26]  Valentina Basarić,et al.  PEDESTRIAN CROSSING BEHAVIOUR AT UNSIGNALIZED CROSSINGS 1 , 2017 .