%0 Conference Proceedings
%T A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios
%+ Centre for Autonomous Systems, University of Lincoln
%+ Guangzhou University
%+ Institute of Neuroscience [Newcastle] (ION)
%A Fu, Qinbing
%A Bellotto, Nicola
%A Wang, Huatian
%A Claire Rind, F.
%A Wang, Hongxin
%A Yue, Shigang
%Z Part 3: Autonomous Vehicles - Aerial Vehicles
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-559
%P 67-79
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19823-7_5
%K LGMD
%K Collision detection
%K Adaptive inhibition mechanism
%K Vehicle crash
%K Complex dynamic scenes
%Z Computer Science [cs]Conference papers
%X This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust’s visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02331344/document
%2 https://inria.hal.science/hal-02331344/file/483292_1_En_5_Chapter.pdf
%L hal-02331344
%U https://inria.hal.science/hal-02331344
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-559