Attention-Based Vision for Autonomous Vehicles

This SBIR Phase I project will demonstrate the feasibility and effectiveness of novel biologically-inspired visual attention models for on-board exploitation of sensor data streams to enable autonomous missions in complex unknown environments. The key innovation in this effort is a principled and biologically plausible computational model for visual attention that integrates both bottom-up (unsupervised) and top-down (context and task-driven) saliency to guide attention. The proposed model enables onboard UGV perception systems to perform context-based and task-driven identification and temporally consistent labeling of objects of interest in real-time. The attention model will enable UGV systems to i) efficiently process incoming sensory data, ii) identify salient features in data streams, iii) automatically learn task relevance from observations, iv) adapt to new scenarios, and v) use spatio-temporal context and task relevance to improve recognition performance in complex environments. The Phase I effort will include; development of proposed attention framework, solution of UGV problems using the models, quantitative and qualitative evaluation of the proposed technologies, and demonstration of proof of concept using real-world data from multiple use-cases. The project will benefit from the UCSDs expertise in visual attention and salience and Novateur Research Solutions experience in sensor exploitation and onboard processing.