  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.
