Bio-inspired vision mimetics toward next-generation collision-avoidance automation
Gary J.W. Xu, Kun Guo, Seop Hyeong Park, Poly Z. H. Sun, Aiguo Song · The Innovation · 2022
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 (eg, human visual, auditory, and tactile systems) and the knowledge learned from daily driving tasks, a high-level cognitive system is developed for integrating collision sensing and collision avoidance.