Extending driver's horizon through comprehensive incident detection in vehicular networks

In this paper, based on principal component analysis (PCA), a comprehensive and efficient incident detection approach that uses probabilistic network and processing methodologies to exploit spatial and temporal correlations and dependencies in vehicular networks, and therefore derive a reliable picture of the driving context, is proposed. The proposed approach provides an integrated way of effectively processing and organizing accumulated spatiotemporal information from a variety of different locations, vehicles, and sources and integrates it into a comprehensive outcome. The use of a PCA-based approach aims at reducing the dimensionality of the data set in which there is a large number of interrelated variables while retaining as much as possible of the variation present in the data set. The operational effectiveness of our proposed incident detection methodology is evaluated via modeling and simulation under different scenarios that represent a wide area of incidents, which range from accident occurrences to alterations in traffic patterns.