Travel Tech Podcast

What’s Actually Stopping Air Taxis From Taking Off

Travel Tech Podcast

Show Notes

The US is about to publish rules that let drones fly beyond line of sight routinely — here's what that unlocks.

Part 108, the FAA's upcoming rulemaking for beyond visual line of sight (BVLOS) operations, is set to change the economics of commercial drone flight. For the first time, operators will have a clear regulatory path to fly without visual observers — making routine, scalable drone operations commercially viable.

Kraettli L. Epperson, Co-Founder and CEO of Vigilant Aerospace, has spent years building the detect-and-avoid systems that make this possible. His focus isn't the drone itself — it's the invisible layer of data, sensors, and safety logic that allows autonomous aircraft to share airspace without introducing unacceptable collision risk.

This episode unpacks what Part 108 actually enables, why detect-and-avoid is the gating technology, and what still needs to happen before drones — and eventually air taxis — can operate at scale.

What You’ll Learn Detect-and-avoid is the gating factor for scale: Autonomous flight is limited not by hardware, but by the ability to safely manage shared airspace. BVLOS is where real commercial value begins: Moving beyond visual line of sight unlocks scalable use cases, but requires regulatory approval and robust safety systems. Airspace awareness depends on data fusion: Combining multiple data sources—transponders, radar, telemetry—is essential to build a reliable picture of the sky. Non-cooperative aircraft create real risk: Not every aircraft broadcasts its position, requiring fallback systems like radar and acoustic detection. Regulation defines what’s commercially viable: FAA frameworks like Part 107 and upcoming Part 108 directly shape what operators can and cannot do. Routine operations require predictability: Businesses invest when operations become repeatable, not just technically possible. Autonomy is an infrastructure problem: The future of aviation depends on invisible systems coordinating decisions in real time, not just smarter vehicles.

Time-Stamped Highlights (03:02) Why Detect-and-Avoid Became the Industry Bottleneck (07:09) From NASA Research to Commercial Safety Systems (09:07) Why Collision Avoidance Is Technically Complex (12:05) Beyond Visual Line of Sight as the Key Unlock (17:09) The Gradual Shift Toward Autonomous Operations (18:59) Real Constraints on Range, Altitude, and Scale (20:21) What Changes When Flying Becomes Routine (24:05) The Challenge of Non-Cooperative Aircraft (28:06) Managing Tradeoffs Between Different Airspace Users (31:08) Where Radar Fits in Drone Safety Systems (39:34) How Air Taxis Fit Into the Same Safety Framework (42:45) What a Fully Integrated Airspace Could Look Like by 2035

Guest Kraettli L. Epperson — Co-Founder and CEO, Vigilant Aerospace Kraettli L. Epperson is the Co-Founder and CEO of Vigilant Aerospace, a company focused on detect-and-avoid and airspace management systems for drones and advanced air mobility. With a background in software, data systems, and entrepreneurship, he works at the intersection of aviation safety, autonomy, and regulation—helping enable scalable, routine drone operations. LinkedIn: https://www.linkedin.com/in/klepperson/ Company: https://www.linkedin.com/company/vigilantaero/ About the Podcast The Travel Tech Podcast features long form conversations with leaders across travel and technology. The show explores how software, data, operations, and distribution come together in real businesses, with an emphasis on tradeoffs, incentives, and lessons that transfer beyond any single company or role. Host Alex Brooker — Founder, Airside Labs Alex is an engineer, technology leader, and founder with deep expertise in mission-critical systems and AI oversight. He leads Airside Labs, an AI business that applies aviation-grade testing and compliance rigor to enterprise AI systems, helping organizations build and test AI agents in regulated environments. Before founding Airside Labs, Alex built and scaled c

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