LGMD and DSNs neural networks integration for collision predication

Zhang, Guopeng, Zhang, Chun and Yue, Shigang (2016) LGMD and DSNs neural networks integration for collision predication. In: Neural Networks (IJCNN), 2016 International Joint Conference on, 24 - 29 July 2016, Vancouver, BC, Canada.

LGMD and DSNs neural networks integration for collision predication
[img] PDF
Zhang-2016-LGMD and DSNs Neural Networks Integration for Collision Predication-v2.1.pdf - Whole Document

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


An ability to predict collisions is essential for current vehicles and autonomous robots. In this paper, an integrated collision predication system is proposed based on neural subsystems inspired from Lobula giant movement detector (LGMD) and directional selective neurons (DSNs) which focus on different part of the visual field separately. The two type of neurons found in the visual pathways of insects respond most strongly to moving objects with preferred motion patterns, i.e., the LGMD prefers looming stimuli and DSNs prefer specific lateral movements. We fuse the extracted information by each type of neurons to make final decision. By dividing the whole field of view into four regions for each subsystem to process, the proposed approaches can detect hazardous situations that had been difficult for single subsystem only. Our experiments show that the integrated system works in most of the hazardous scenarios.

Keywords:Mobile Robots, Visualization, Photoreceptors, Biological neural networks, Vehicles, Collision avoidance, Mathematical model, neural nets
Subjects:H Engineering > H670 Robotics and Cybernetics
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
Related URLs:
ID Code:27955
Deposited On:11 Aug 2017 09:26

Repository Staff Only: item control page