Assured safe-separation is essential for achieving seamless high-density operation of airborne vehicles in a shared airspace. To equip resource-constrained aerial systems with this safety-critical capability, we present ViSafe, a high-speed vision-only airborne collision avoidance system. ViSafe offers a full-stack solution to the Detect and Avoid (DAA) problem by tightly integrating a learning-based edge-AI framework with a custom multi-camera hardware prototype designed under SWaP-C constraints. By leveraging perceptual input-focused control barrier functions (CBF) to design, encode, and enforce safety thresholds, ViSafe can provide provably safe runtime guarantees for self-separation in high-speed aerial operations. We evaluate ViSafe's performance through an extensive test campaign involving both simulated digital twins and real-world flight scenarios. By independently varying agent types, closure rates, interaction geometries, and environmental conditions (e.g., weather and lighting), we demonstrate that ViSafe consistently ensures self-separation across diverse scenarios. In first-of-its-kind real-world high-speed collision avoidance tests with closure rates reaching 144 km/h, ViSafe sets a new benchmark for vision-only autonomous collision avoidance, establishing a new standard for safety in high-speed aerial navigation.
@article{arxiv.2505.03694,
title = {Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid},
author = {Parv Kapoor and Ian Higgins and Nikhil Keetha and Jay Patrikar and Brady Moon and Zelin Ye and Yao He and Ivan Cisneros and Yaoyu Hu and Changliu Liu and Eunsuk Kang and Sebastian Scherer},
journal= {arXiv preprint arXiv:2505.03694},
year = {2025}
}