Title: A margin-based approach to vehicle threat assessment

Authors: Alexandre Constantin; Junghee Park; Karl Iagnemma

Addresses: Department of Mechanical Engineering, Massachussets Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA ' Department of Mechanical Engineering, Massachussets Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA ' Department of Mechanical Engineering, Massachussets Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA

Abstract: In this paper we propose a novel approach to the threat assessment problem for Advanced Driver Assistance System (ADAS) and autonomous navigation decision making support. This threat assessment is based on estimation of the control margin afforded to a vehicle and is performed in a multi-threat framework. Given sensor information available about the surrounding environment, an algorithm first identifies corridors of travel through which the vehicle can safely navigate. The second stage then assesses the threat posed to the vehicle in each identified corridor via a metric associated with available control margin. For this purpose, the corridors are approximated by sets of trajectories generated from a lattice sampled in the vehicle's input space. The level of threat can then serve to influence autonomous navigation as an input to a decision-making layer. It also potentially allows a semi-autonomous control system to honour driver intent while ensuring safe and robust navigation in hazardous events. The benefit of such an approach is compared to common threat metrics in canonical scenarios. The method is also applied to the multi-lane road environment of highway navigation by post processing human driving data gathered from a simulator.

Keywords: vehicle threat assessment; driving assistance systems; homotopy class; lattice motion planning; driver assistance; autonomous navigation; control margin estimation; vehicle safety; hazardous events.

DOI: 10.1504/IJVAS.2014.067869

International Journal of Vehicle Autonomous Systems, 2014 Vol.12 No.4, pp.384 - 411

Received: 02 Jul 2014
Accepted: 27 Jan 2015

Published online: 07 Mar 2015 *

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