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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(5): 248-255 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.5.606

Explaining Potential Risks in Traffic Scenes by Combining Logical Inference and Physical Simulation

Ryo Takahashi, Naoya Inoue, Yasutaka Kuriya, Sosuke Kobayashi, and Kentaro Inui
Abstract—The automatic recognition of risks in traffic scenes is a core technology of Advanced Driver Assistance Systems (ADASs). Most of the existing work on traffic risk recognition has been conducted in the context of motion prediction of vehicles. Thus, existing systems rely on directly observed information (e.g., velocity), whereas the exploitation of implicit information inferable from observed information (e.g., the intention of pedestrians) has rarely been explored. Our previous approach used abductive reasoning to infer implicit information from observation and jointly identify the most-likely risks in traffic scenes. However, abductive frameworks do not exploit quantitative information explicitly, which leads to a lack of grounds for physical quantities. This paper proposes a novel risk recognition model combining first-order logical abduction-based symbolic reasoning with a simulation based on physical quantities. We build a novel benchmark dataset of real-life traffic scenes that are potentially risky. Our evaluation demonstrates the potential of our approach. The developed dataset has been made publicly available for research purposes.

Index Terms—Advanced driver assistance system (ADAS), logical inference, physics simulation.

Ryo Takahashi, Naoya Inoue, Sosuke Kobayashi, and Kentaro Inui are with the Graduate School of Information Sciences, Tohoku University (e-mail: {ryo.t, naoya-i, sosuke.k, inui}@ecei.tohoku.ac.jp). Yasutaka Kuriya is with Denso Corporation (e-mail: YASUTAKA_KURIYA@denso.co.jp).

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Cite: Ryo Takahashi, Naoya Inoue, Yasutaka Kuriya, Sosuke Kobayashi, and Kentaro Inui, "Explaining Potential Risks in Traffic Scenes by Combining Logical Inference and Physical Simulation," International Journal of Machine Learning and Computing vol. 6, no. 5, pp. 248-255, 2016.

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