Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid

Abstract

Maintaining visual separation is crucial to achieving safe and seamless high-density operation of airborne vehicles in shared airspace, where pilots currently shoulder this responsibility. To automate this, we present ViSafe, a high-speed airborne vision-only collision avoidance system. Designed under SWaP-C constraints, ViSafe is built using a tightly integrated learning-enabled edge-AI framework deployed on a custom multi-camera hardware prototype, offering a full-stack solution to the Detect and Avoid (DAA) problem. By leveraging perceptual input-focused control barrier functions (CBF) to design, encode, and enforce safety thresholds, ViSafe can provide provably safe runtime guarantees on self-separation for high-speed aerial operations. We evaluate ViSafe’s performance through an extensive test campaign involving both simulated digital-twin 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/hr, ViSafe sets a new benchmark for vision-only autonomous collision avoidance, establishing a new standard for safety in high-speed aerial navigation.