Paper Info
Title
Probabilistic Decision-Making At Road Intersections: Formulation And Quantitative Evaluation
Abstract
As drivers approach a road intersection, they must decide whether to cross it or to come to a stop. For this purpose, drivers make a situation assessment and adapt their behaviour accordingly. When this task is performed by a computer, the available information is partial and uncertain. Any decision requires the system to use this information as well as taking into account the behaviour of other drivers to avoid collisions. Common metrics such as collision rate can remain low in an interactive environment because of other driver's actions. Consequently, evaluation metrics must depend on other driving aspects. In this paper a decision-making mechanism and metrics to evaluate such a system at road intersection crossing are presented. For the former, a Partially Observable Markov Decision Process is used to model the system with respect to uncertainties in the behaviour of other drivers. For the latter, different key performance indicators are defined to evaluate the resulting behaviour of the system with different configurations and scenarios. The approach is demonstrated within an automotive grade simulator. It has showed at times, that whilst the vehicle can cross safely the intersection, it might not satisfy other key performance indicators related to highway code.
Year
DOI
Venue
2018
10.1109/ICARCV.2018.8581203
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV)
Field
DocType
ISSN
Performance indicator,Computer science,Partially observable Markov decision process,Operations research,Situation analysis,Control engineering,Collision,Probabilistic logic,Automotive industry
Conference
2474-2953
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Mathieu Barbier111.07
Christian Laugier213314.37
Olivier Simonin323133.99
Javier Ibanez-Guzmin400.34