Detecting Road Events Using Distributed Data Fusion: Experimental Evaluation for the Icy Roads Case

Abstract

International audienceOne of the main ideas in the area of intelligent transport systems is to use all possible information, coming from vehicles and infrastructure, in order to make the system " smarter " and avoid potentially dangerous situations – collisions, accidents, bottlenecks... However data is sometimes unreliable due to source and communication network quality, leading vehicles or even the whole system to wrong decisions. We present a generic method for detecting dangerous events on the road. To support unreliable data sources, it uses distributed data fusion. Moreover, to deal with network failures, it relies on a self-stabilizing generic distributed algorithm. Our method mixes measurements obtained from vehicle onboard sensors as well as wireless sensors placed close to the road and connected to road side units. Each vehicle computes how confident it is about a potential dangerous event using both local and remote data. To evaluate our approach, we implemented it using a specific hardware and software platform. Moreover, we instantiated a simple, yet efficient application to detect icy roads, based on temperature measurements. Thanks to both in-lab and actual on-the-road experiments, we demonstrate the possibility to deduce proper results from unreliable data and, consequently, the correctness and usefulness of our approach

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On the security of warning message dissemination in vehicular Ad hoc networks

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Study of Discounting Methods Applied to Canonical Decomposition of Belief Functions

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