Abstract
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.
Video
System Design
Overview of our ViSafe framework for real-world testing and hardware-in-the-loop simulation: Firstly, the onboard sensors or digital twin simulation stream the multi-cam videos to the AirTrack visual detection module, which detects the intruder across multiple views. Then, these detections, along with the ownship state information, are sent to the multi-view fusion and coordinate frame conversion module, which then tracks the intruder and sends the intruder state information along with the ownship state information to the CBF. The CBF uses the nominal global plan and the safety violation assessment to compute modifications to the nominal control input in case of violation. This safe control output is then sent to the drone autopilot system for execution. This loop continues to operate in real-time until the flight test is complete.
Diversity of Testing Scenarios
(a) The various encounter geometries used for real-world & simulation flight testing. (b) The diverse weather and lighting conditions that were used to evaluate ViSafe's robustness in simulation. In the simulation, based on the chosen encounter geometry, the intruder position is sampled randomly on the flying corridor.
BibTeX
@article{kapoor2025demonstrating,
title={Demonstrating ViSafe: Vision-enabled Safety for High-speed Detect and Avoid},
author={Kapoor, Parv and Higgins, Ian and Keetha, Nikhil and Patrikar, Jay and Moon, Brady and Ye, Zelin and He, Yao and Cisneros, Ivan and Hu, Yaoyu and Liu, Changliu and Kang, Eunsuk and Scherer, Sebastian},
booktitle={Robotics: Science and Systems (RSS)},
year={2025}
}