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Autonomous vehicles are unlikely to make fleet tracking obsolete. Instead, they can expand it from showing vehicle locations and conventional telematics data into a remote-operations layer for monitoring, dispatch, incident management, and response coordination. The shift depends on the vehicle and deployment: an automated driving system is not the same as a driver-assistance feature, and there is no established industry-wide figure for how automation changes fleet costs, staffing, or productivity.
What counts as an autonomous vehicle?
For this discussion, an autonomous vehicle means one equipped with an automated driving system (ADS) intended to perform the driving task at SAE Levels 3–5. The National Highway Traffic Safety Administration (NHTSA) distinguishes these systems from driver-assistance features that support a human driver who remains responsible for monitoring the road. Adaptive cruise control or lane-keeping assistance alone does not make a vehicle a driverless fleet vehicle. See NHTSA’s automated driving systems guidance.
The distinction matters operationally: a conventional fleet tracking team generally monitors vehicles whose drivers handle the driving task, while an ADS deployment may also require a process for remote monitoring or assistance. The exact responsibilities depend on the vehicle and its operating model.
How autonomous vehicles could change fleet tracking
From location checks to remote operations
Tracking systems can become part of a broader operational picture: where a vehicle is, what assignment it is serving, whether it has stopped or needs help, and what action an operator should take. NHTSA identifies fleet monitoring and incident management as deployment challenges. A 2021 report by Mary Cummings, Songpo Li, Dev Seth, and Matthew Seong also examines remote monitoring and dispatch concepts for autonomous passenger and freight fleets, including OEM support, robo-taxi, trucking, public transportation, and public-agency operations-center models. These are researched concepts of operation, not evidence that every fleet already runs a remote operations center. Read the CSCRS report on remote AV dispatch.
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More value—and more demands—from location data
A map pin is useful, but it may not provide enough context for safe or effective operations. The UK Geospatial Commission says automated vehicles need trustworthy, precise information about the road network and identifies missing or imprecise data, accuracy standards, and data-sharing practices as areas that need attention. Applied to fleet tracking, that means operators may need location information that is timely and suitable for the task, plus systems able to exchange relevant data. This is an operational implication of the Commission’s findings, not a claim that every fleet platform already supplies those capabilities. See the Commission’s report on positioning for the future of transport.
Location data remains important; it does not perform the perception, decision-making, or safety functions of an ADS. GPS fleet tracking can show where a vehicle is, but it should not be treated as a substitute for the vehicle’s automated driving technology.
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Dispatch work and staffing may need to be redesigned
Remote operations can add exception handling and AV-specific tasks to dispatchers’ existing workload. In the CSCRS report’s simulation, a single dispatcher could be overloaded by even minimal AV-related tasks under the case study’s modeled normal conditions. The authors caution that this result is specific to that case study. It is a reason to model workload and staffing for a particular deployment—not a universal staffing rule.
Incident plans must account for first responders
Fleet operators should establish how vehicle status will be communicated and who will coordinate with responders if an ADS-dedicated vehicle is involved in an emergency or another roadside interaction. SAE’s 2024 AVSC best practice addresses first-responder interactions with fleet-managed Level 4 and Level 5 ADS-dedicated vehicles. It identifies direct, indirect, and informational interactions and offers an interaction-planning framework for developers, manufacturers, and fleet operators. The document is SAE AVSC-I-01-2024.
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How conventional tracking and AV operations differ
| Area | Conventional fleet tracking | Potential AV operations emphasis |
|---|---|---|
| Responsibility | A human driver performs the driving task and is responsible for monitoring. | An ADS performs the driving task within its operating conditions; remote assistance or monitoring arrangements may also be relevant. |
| Location and data | Vehicle position and conventional fleet data support visibility and day-to-day management. | Operators may also need suitable road context, current data, and interoperable information exchange. |
| Workflow | Routine location checks and dispatch for driver-operated vehicles. | Remote monitoring, exception handling, assistance, and incident management may become part of the workflow. |
| Staffing | Dispatch workload reflects existing fleet tasks and processes. | AV-specific work can affect workload; one simulation found overload in its modeled case, not a general staffing threshold. |
| Safety coordination | Standard fleet response procedures apply to driver-operated vehicles. | Plans may need to cover how fleet operators and responders interact with ADS-dedicated vehicles. |
What current adoption figures do—and do not—show
Existing fleet-technology surveys provide context for current tracking and telematics use, but they do not measure the effect of autonomous vehicles on tracking. Keep the figures attributed to their publishers and within their stated populations:
- Verizon Connect’s 2026 report says 80% of surveyed fleet professionals rely on GPS fleet-tracking technology. It draws on insights from almost 900 fleet professionals across government, commercial, and enterprise sectors. This is a company-published survey result, not a government estimate or an AV-fleet-specific measurement.
- The same Verizon Connect report says 46% of surveyed fleet professionals use video telematics, a 10-percentage-point increase in adoption since 2023. This also concerns fleets generally, not an autonomous-vehicle effect.
- Geotab’s 2026 report says 72% of European fleets use near-real-time location intelligence, based on a survey of 1,800 fleet managers and mobility experts across European industries. It is a vendor-reported European survey figure, not a measurement of autonomous fleet adoption.
These numbers describe a broader fleet-technology baseline. They do not establish how much autonomous vehicles increase or reduce tracking costs, staffing needs, system adoption, or productivity across the industry. For current activity in the United States, NHTSA’s September 2026 report to Congress covers agency work from January through June 2026, including safety oversight, remote assistance, fleet monitoring, and incident management. It also states that the proposed AV STEP rulemaking was withdrawn on June 26, 2026; it should not be described as an active requirement.
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What fleet operators can prepare for
- Define whether the vehicles in scope use driver assistance or an ADS, and specify the operational responsibilities associated with each.
- Map what dispatchers need to see and do when a vehicle stops, requests assistance, or encounters an incident.
- Assess whether location and road-context data meet the intended use, and determine how relevant information will be exchanged across systems.
- Model dispatcher workload for the actual fleet, operating conditions, and response process rather than applying one case study’s staffing result universally.
- Plan first-responder interactions, including how vehicle status and operational contacts will be made available when needed.
For infrastructure owner-operators considering automation in their own fleets, the National Academies’ Automated Applications for Infrastructure Owner-Operator Fleets (NCHRP Research Report 1119, 2024) examines implementation status and barriers. Its scope is specific to infrastructure owner-operator fleets, rather than general fleet tracking.
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