Xu, Gary J.W., Guo, Kun, Park, Seop Hyeong , Sun, Poly Z.H. and Song, Aiguo (2023) Bio-inspired vision mimetics towards next generation collision avoidance automation. The Innovation, 4 (1). ISSN 2666-6758
Full content URL: https://doi.org/10.1016/j.xinn.2022.100368
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The Innovation 2023-100368.pdf - Whole Document Available under License Creative Commons Attribution 4.0 International. 580kB |
| Item Type: | Article |
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| Item Status: | Live Archive |
Abstract
The current “deep learning + large-scale data + strong supervised labeling” technology framework of collision avoidance for ground robots and aerial drones is becoming saturated. Its development gradually faces challenges from real open-scene applications, including small data, weak annotation, and cross-scene. Inspired by the neural structure and processes underlying human cognition (e.g., human visual, auditory, and tactile systems) and the knowledge learned from daily driving tasks, such as, a high-level cognitive system is developed for integrating collision sensing and collision avoidance. This bio-inspired cognitive approach takes the advantages of good robustness, high self-adaptability, and low computation consumption in practical driving scenes.
| Subjects: | C Biological Sciences > C830 Experimental Psychology |
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| Divisions: | College of Social Science > School of Psychology |
| Related URLs: | |
| ID Code: | 53112 |
| Deposited On: | 24 Jan 2023 16:46 |
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