A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

Understand

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.