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Olav Madland, CEODeploying autonomous vehicles as a transport service involves more than independent driving. Operators must integrate them into existing dispatch systems, coordinate them with conventional vehicles and manage unexpected situations. Scaling these services requires a way to manage different vehicle types within existing transport operations.
Applied Autonomy, part of the INIT Group, helps operators integrate autonomous vehicles from different manufacturers and autonomous driving system providers into their existing transport operations through xFlow, its vehicle-agnostic software platform.
xFlow gives dispatchers a shared view of autonomous from different vendors, including their locations, status and estimated arrival times and provides the flexibility operators are looking for. It also provides a common operational layer for remote assistance, passenger support and contingency management, enabling operators to establish consistent procedures for situations that exceed a vehicle’s autonomous driving capabilities. With interfaces to third-party systems for on-demand and scheduled services, xFlow operators can introduce autonomous vehicles gradually while continuing to use their existing dispatch systems.
“We work with operators from day one, supporting their use cases and making the transition to autonomous transport easier and efficient,” says Olav Madland, CEO.
Standardising Remote Operations across Mixed Fleets
Applied Autonomy works with operators that have a clear plan to scale beyond a pilot. The company helps define deployment strategies and provides API specifications and interface requirements that operators can share with vehicle manufacturers before selecting and connecting vehicles. These requirements establish how vehicles can connect to xFlow, which supports varied communication standards provided that integrations comply with cybersecurity and GDPR requirements.
Once connected, vehicles can be managed through consistent procedures without separate systems or different operating skills for each manufacturer. Depending on the operation, a single remote assistant can oversee up to 15 vehicles, reducing the need for an onboard driver in every vehicle. When a vehicle encounters an obstruction, like road works, it can request remote assistance, including remote driving, where supported by the vehicle’s technology. xFlow coordinates the secure handover to the remote driver, records why the intervention was needed and who assumed responsibility and documents the transfer of control back to the autonomous vehicle.
Moving from Safety Drivers to Driverless Operations
Applied Autonomy has developed xFlow across different vehicle types, including mobility shuttles, airport baggage trucks and port logistics vehicles. These deployments retained onboard safety drivers until a bus operation in Stavanger demonstrated progress towards driverless service.
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We work with operators from day one, supporting their use cases and making the transition to autonomous transport easier and efficient.
For the Stavanger project, Applied Autonomy integrated a Karsan bus equipped with autonomous driving technology from ADASTEC with xFlow for remote management. The bus operated without interventions from its onboard safety driver, while operational data stored in xFlow helped the operator understand the performance of the vehicle and the manufacturer assess its operation. By April 2026, the operator had removed the safety driver and expressed readiness to scale the service with more vehicles, marking an important step toward driverless public transport operations.
Building on this progress, Applied Autonomy sees public transport agencies and operators across Europe as a key market as they respond to driver shortages and explore more flexible mobility options. xFlow with INIT ITCS supports the introduction of autonomous vehicles into existing transport networks and enables on-demand services where fixed routes and timetables fall short.
Airport mobility and logistics offer another opportunity, particularly where operators need to coordinate different types of autonomous vehicles within a single operation. At Frankfurt Airport, a planned mixed-fleet deployment is intended to bring large buses, passenger shuttles and logistics vehicles into a shared operational framework. The operator aims to scale the deployment by 2030, with the project expected to begin when the required vehicles become available.
By connecting vehicles to existing systems and standardising remote assistance, Applied Autonomy helps operators move from individual deployments towards larger, integrated transport services. Its recognition as the Top Autonomous Transport Software Platform in Europe 2026 reflects this focus on making mixed-fleet operations practical to scale.
Autonomous Fleet Integration beyond the Pilot Stage
An autonomous shuttle may complete its route while leaving a transport operator with a difficult procurement problem. Its dispatch interface may sit outside the control room, its intervention procedures may depend on the vehicle manufacturer and its data may not reach the systems used to manage conventional services. Adding another vehicle brand can multiply those divisions. Software procurement must account for the transport network that will run around the vehicle, not simply the vehicle's driving performance.
Fleet integration begins at the dispatch desk. Public transport operators rarely replace their existing vehicles at once, and autonomous services may initially cover only a portion of scheduled routes. A platform that requires dispatchers to open a separate console for every new vehicle creates extra coordination work. Buyers should establish whether autonomous assignments can enter traffic management systems and whether vehicle location, estimated arrival times and service status return in formats those systems can use. Compatibility with conventional fleets allows capacity changes to be managed within familiar workflows.
Vehicle independence also has contractual consequences. A successful pilot built around one manufacturer's proprietary interface can restrict later tenders, when larger buses or logistics vehicles enter the fleet. Procurement specifications should define the required application programming interfaces before vehicle selection. The software provider's ability to accommodate different communication standards must be assessed alongside cybersecurity controls and data protection requirements. An open integration approach is valuable only when the resulting connections remain governed and supportable.
“Applied Autonomy’s integration specifications help buyers set API requirements before ordering vehicles, while its remote assistance functions establish procedures across vehicle types.”
Removing the onboard driver changes the work for supervisors. Remote staff may need to respond to a vehicle unable to proceed, assist passengers or initiate a controlled handover to a remote driver. The autonomous driving system and the remote-driving equipment do not come from the fleet management software supplier. Contract documents should distinguish those responsibilities. Clear handover procedures must establish when driving authority changes, who accepts it and how the return to autonomous control is recorded. Without that record, a dashboard can display a fleet without adequately accounting for interventions.
Staffing estimates deserve discipline. A remote supervisor may oversee several vehicles, but the achievable ratio depends on how often vehicles request intervention and how those requests overlap. Buyers should examine alert prioritisation, contingency procedures and the evidence retained after each event rather than accept a fixed vehicles-per-operator figure. Historical intervention data can inform decisions about expanding driverless hours or moving from supervised trials to regular service.
A procurement plan should extend beyond the demonstration route. Agencies introducing on-demand services beside fixed timetables, or airports combining passenger mobility with goods movement, may need vehicle types later. Testing how the platform will incorporate those services can prevent a short pilot from dictating the architecture of a larger fleet.
Applied Autonomy develops xFlow as a software layer connecting autonomous vehicles to existing transport management systems. It supports mixed fleets by exchanging dispatch information and vehicle status with the operator's tools, rather than requiring separate workflows for each manufacturer. Its integration specifications help buyers set API requirements before ordering vehicles, while the remote assistance functions establish procedures across vehicle types. xFlow also documents transfers of driving responsibility when a vehicle requires remote intervention, including the circumstances and personnel involved. Service data supports decisions about removing onboard supervision and expanding deployment. Its approach is particularly relevant to transit authorities and airport operators introducing multiple autonomous vehicle types alongside conventional services.
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