Detection of Unusual Behaviours for Estimation of Context Awareness at Road Intersections

Alexandre Armand 1, 2 David Filliat 2, 3 Javier Ibanez-Guzman 1
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : In general, Advanced Driving Assistance Systems (ADAS) warn drivers once a high risk situation has been inferred. This is made under the assumption that all drivers react in the same manner. However, it is not the case as drivers react as a function of their own driving style. This paper proposes a framework which allows the estimation of the degree of awareness with regard to the focus object of the context that is governing the vehicle behaviour (e.g. the arrival to an intersection). The framework learns the manner in which individual drivers behave for a given context, and then detects whether or not the driver is behaving differently under similar conditions. In this paper the principles of the framework are applied to a fundamental use-case, the arrival to a stop intersection. Results from experiments under controlled conditions are included. They show that the formulation allows for a coherent estimation of the driver awareness while approaching to such intersections.
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Alexandre Armand, David Filliat, Javier Ibanez-Guzman. Detection of Unusual Behaviours for Estimation of Context Awareness at Road Intersections. 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Nov 2013, Tokyo, Japan. pp.313-318, Proceedings of the 5th Workshop on Planning, Perception and Navigation for Intelligent Vehicles. 〈hal-01215563〉

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