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

arXiv: https://arxiv.org/abs/2505.03694 · HTML: https://mlsanity.com/paper/2505.03694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 18 of 18)

- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [Safety Index Synthesis via Sum-of-Squares Programming](https://mlsanity.com/paper/2209.09134/) (2022)
- [Flying Objects Detection from a Single Moving Camera](https://mlsanity.com/paper/1411.7715/) (2014)
- [Pegasus Simulator: An Isaac Sim Framework for Multiple Aerial Vehicles Simulation](https://mlsanity.com/paper/2307.05263/) (2023)
- [Robust Control Barrier Functions under High Relative Degree and Input Constraints for Satellite Trajectories](https://mlsanity.com/paper/2107.04094/) (2021)
- [FoundLoc: Vision-based Onboard Aerial Localization in the Wild](https://mlsanity.com/paper/2310.16299/) (2023)
- [TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations](https://mlsanity.com/paper/2403.03372/) (2024)
- [Learning Calibrated-Guidance for Object Detection in Aerial Images](https://mlsanity.com/paper/2103.11399/) (2021)
- [Collision Avoidance and Geofencing for Fixed-wing Aircraft with Control Barrier Functions](https://mlsanity.com/paper/2403.02508/) (2024)
- [AirObject: A Temporally Evolving Graph Embedding for Object Identification](https://mlsanity.com/paper/2111.15150/) (2021)
- [Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller](https://mlsanity.com/paper/2404.01184/) (2024)
- [AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator](https://mlsanity.com/paper/2306.11203/) (2023)
- [Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid](https://mlsanity.com/paper/1903.03275/) (2019)
- [Challenges in Close-Proximity Safe and Seamless Operation of Manned and Unmanned Aircraft in Shared Airspace](https://mlsanity.com/paper/2211.06932/) (2022)

## Built on by (most cited first, 1 of 1)

- [Constrained Decoding for Safe Robot Navigation Foundation Models](https://mlsanity.com/paper/2509.01728/) (2025)

## Abstract (arXiv)

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.