Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function

Shlomi Hacohen

doi:10.1016/j.trc.2017.10.024

2018-01-01

PRE

Shlomi Hacohen

Nir Shvalb *

Shraga Shoval

DOI：10.1016/j.trc.2017.10.024

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摘要

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More than 1.2 million people die in road crashes each year, and more than 20 million are severely injured, making it the 9th leading cause of death in the world (2.2% of all deaths globally). Pedestrian deaths comprise more than 35% of road accident deaths, mostly as a result of pedestrian-vehicle crashes. This paper proposes a new model for formulating the dynamics of the interaction between drivers and pedestrians at congested conflict spots where drivers and/or pedestrians do not closely follow the traffic laws and regulations. In this type of spots, characterized by heavy traffic, pedestrians and vehicles interact in close proximity, often requiring sharp and aggressive maneuvers to avoid crashes. The model is based on the Probabilistic Navigation Function (PNF), originally developed for robotics motion planning, that constructs a trajectory according to the probabilistic collision risks. According to this model, pedestrians construct a virtual risk map that assigns the entire crossing area with probabilities for a collision with vehicles, and then select their actions based on their perceived probability for collision. Many accidents can be interpreted in terms of the proposed model, either as a result of incorrect perception of risks, or, despite proper estimation of risks, by a wrong choice of collision maneuvers. The development of the model follows a theoretical and experimental investigation of pedestrian/vehicle interactions at crosswalks. The model is implemented in an agent-based simulation system for pedestrian/driver interaction, and is validated using video clips taken at several congested road spots. It can be used for analyzing the effect of changes in location architecture and traffic regulations for each spot. The model can also serve as a standard tool in simulations for assessing accident risks in urban environments. Finally, it can be utilized in control systems of autonomous vehicles and in drivers' on-board alert systems.

Keyword:

Probability for collision

Perception model

Pedestrians safety

Accidents avoidance

Motion planning