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Title: Developing an advanced collision risk model for autonomous vehicles
Authors: Katrakazas, Christos
Issue Date: 2017
Publisher: © Christos Katrakazas
Abstract: Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.]
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
Sponsor: EPSRC.
URI: https://dspace.lboro.ac.uk/2134/27538
Appears in Collections:PhD Theses (Design School)

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