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

Fu, Qinbing and Bellotto, Nicola and Wang, Huatian and Rind, F. Claire and Wang, Hongxin and Yue, Shigang (2019) A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios. In: 15th International Conference on Artificial Intelligence Applications and Innovations, 24-26, May, 2019, Crete, Greece.

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

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.

Keywords:LGMD, collision detection, adaptive inhibition mechanism, vehicle crash, complex dynamic scenes
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
G Mathematical and Computer Sciences > G740 Computer Vision
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35586
Deposited On:17 Apr 2019 09:31

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