Using Visual Abstractions to Improve Spatially Aware Nominal Safety in Autonomous Vehicles

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Virginia Tech


As autonomous vehicles (AVs) evolve, ensuring their safety extends beyond traditional met- rics. While current nominal safety scores focus on the timeliness of AV responses like latency or instantaneous response time, this paper proposes expanding the concept to include spatial configurations formed by obstacles with respect to the ego-vehicle. By analyzing these spatial relationships, including proximity, density and arrangement, this research aims to demon- strate how these factors influence the safety force field around the AV. The goal is to show that beyond meeting Responsibility-Sensitive Safety (RSS) metrics, spatial configurations significantly impact the safety force field, particularly affecting path planning capability. High spatial occupancy of obstacle configurations can impede easy maneuverability, thus challenging safety-critical modules like path planning. This paper aims to capture this by proposing a safety score that leverages the ability of modern computer vision techniques, par- ticularly image segmentation models, to capture high and low levels of spatial and contextual information. By enhancing the scope of nominal safety to include such spatial analysis, this research aims to broaden the understanding of drivable space and enable AV designers to evaluate path planning algorithms based on spatial configuration centric safety levels.



Autonomous Vehicles, Spatial Awareness, Nominal Safety, Convolutional Neural Networks, Semantic Segmentation