Pedestrian Vision and Collision Avoidance Behavior: Investigation of the Information Process Space of Pedestrians Using an Eye Tracker

This study investigates the Information Process Space (IPS) of pedestrians, which has been widely used in microscopic pedestrian movement simulation models. IPS is a conceptual framework to define the spatial extent within which all objects are considered as potential obstacles for each pedestrian when computing where to move next. Particular foci of our study were on identifying the size and shape of IPS through examining observed gaze patterns of pedestrians. A series of experiments were conducted in a controlled laboratory environment, in which up to 4 participants walked on a platform at their natural speed. Their gaze patterns were recorded by a head-mounted eye tracker and walking paths by laser-range-scanner–based tracking systems at the frequency of 25 Hz. Our findings are three folds: pedestrians pay much more attention to ground surface to detect potential immediate environmental hazards than fixating on obstacles; most their fixations fall within a cone-shape area rather than semicircle; the attention paid to approaching pedestrians is not as high as that to static obstacles. These results led to an insight that the structure of IPS should be re-examined by taking directional characteristics of pedestrians’ vision.

[1]  Peter A. Thompson.  Developing new techniques for modelling crowd movement , 1994 .

[2]  A E Patla,et al.  Where and when do we look as we approach and step over an obstacle in the travel path? , 1997, Neuroreport.

[3]  Katsuya Yamori.  Going with the flow : Micro-macro dynamics in the macrobehavioral patterns of pedestrian crowds , 1998 .

[4]  Harry J. P. Timmermans,et al.  A Multi-Agent Cellular Automata System for Visualising Simulated Pedestrian Activity , 2000, ACRI.

[5]  Takashi Chikayama,et al.  Simulating the Collision Avoidance Behavior of Pedestrians , 2000 .

[6]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[7]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[8]  Michael Schreckenberg,et al.  Pedestrian and evacuation dynamics , 2002 .

[9]  Alan Penn,et al.  Encoding Natural Movement as an Agent-Based System: An Investigation into Human Pedestrian Behaviour in the Built Environment , 2002 .

[10]  Shigeyuki Okazaki,et al.  VISUAL SEARCH WHILE CLIMBING OR DESCENDING THE STAIRCASES AT A SUBWAY STATION , 2002 .

[11]  Joan N. Vickers,et al.  How far ahead do we look when required to step on specific locations in the travel path during locomotion? , 2002, Experimental Brain Research.

[12]  M. Bierlaire,et al.  Behavioral Dynamics for Pedestrians , 2003 .

[13]  Serge P. Hoogendoorn,et al.  Simulation of pedestrian flows by optimal control and differential games , 2003 .

[14]  Neal M. Finkelstein,et al.  Exploration of the parameter range of a continuous-space, agent-based model for pedestrian evolution , 2003 .

[15]  Michel Bierlaire,et al.  Simulation of Pedestrian Behaviour using a Discrete Choice Model Calibrated on Actual Motion Data , 2004 .

[16]  Serge P. Hoogendoorn,et al.  Pedestrian Travel Behavior Modeling , 2005 .

[17]  Taras I. Lakoba,et al.  Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution , 2005, Simul..

[18]  T Fujiyama.  Investigating density effects on the "awareness" area of pedestrians using an eye tracker , 2006 .

[19]  How do Elderly Pedestrians Perceive Hazards in the Street? An Initial Investigation towards Development of a Pedestrian Simulation that Incorporates Reaction of Various Pedestrians to Environments , 2007 .