Phase 1 of 6
Scoping & Fleet SLA Constraints
Define the fleet segments, inference-interval budgets, safety posture, and connectivity envelope that will govern every subsequent architectural decision for corridor-edge fleet intelligence.
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Phase Progress
Required Recommended Optional Open-Source Proprietary Trinidy
Fleet Segments & Use-Case Surface
Identify fleet segments in scope for the intelligence platform
Why This Matters
Each segment has a materially different duty cycle, telemetry density, and regulatory surface — long-haul trucks are bound by FMCSA Hours-of-Service rules and ELD mandates, transit carries passenger-safety obligations that field-service fleets do not, and yard vehicles operate on private property outside most NHTSA FMVSS scope. One-sizing a single model architecture across segments wastes the highest-signal features in each segment. The usual failure mode is a platform built for long-haul corridors being retrofitted for urban last-mile, where the corridor-density assumption no longer holds.
Note prompts — click to add
+ Which segments share enough telemetry and duty-cycle overlap to justify a shared model family?+ Have we mapped each segment to its primary regulator (FMCSA, FTA, state PUC) and its SLA expectations?+ Is corridor-edge inference actually the right architecture for urban last-mile, or does it need a different topology?Confirm which commercial fleet segments the tower-edge inference platform must serve.
Select all that apply
Define inference-interval SLA per workload
Why This Matters
Different fleet workloads have different latency physics — a collision-avoidance cue at 65 mph is useless past ~45ms, while predictive maintenance can tolerate minute-scale latency. A single SLA across all three models either over-provisions infrastructure for maintenance or under-provisions it for safety. NHTSA 2024 ADAS guidance treats sub-45ms as the ceiling for actionable highway-speed collision cues; any architecture that cannot hit that budget should not advertise collision avoidance as a feature.
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+ What is our current p99 inference latency per workload, and where is the bottleneck — telemetry ingest, model inference, or egress to fleet API?+ Have we stress-tested under peak corridor density (Class 8 rush at major freight hubs) rather than average load?+ What is our fallback when a tower-edge node is saturated — cloud failover, degraded model, or drop the signal?Select the per-workload inference cadence the platform must hold under peak vehicle density.
Single choice
Trinidy — Cloud-routed inference typically costs 180–350ms round-trip along freight corridors — unusable for safety-adjacent workloads at highway speed. Trinidy runs the three-model stack on tower-edge NEXUS OS nodes at 18–38ms, with corridor density providing continuous coverage.
Classify SAE J3016 automation level per workload
Why This Matters
SAE J3016 is the governing taxonomy for driving automation and is cited directly in NHTSA and UNECE regulations. The documentation and safety-case burden scales steeply from L2 to L3 — L3 is where ISO 21448 SOTIF and ISO 26262 functional-safety evidence stops being optional. A model that merely informs a driver at L0-L2 has a fundamentally different regulatory posture than one that actuates control at L3+.
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+ Is every vehicle in scope classified against J3016, or do we assume one level across the fleet?+ Do our AI outputs actuate control (L3+) or only inform (L0-L2), and is that documented per workload?+ Have we mapped each automation level to its required ISO 26262 ASIL and ISO 21448 SOTIF evidence?Confirm the SAE automation level the fleet vehicles operate at — this drives the safety-case burden.
Single choice
Define functional-safety posture under ISO 26262 / ISO 21448
Why This Matters
ISO 26262 covers functional safety of E/E systems and assigns ASIL levels (A through D) that drive the entire V&V evidence chain. ISO 21448 SOTIF covers the safety of the intended function — specifically the unknown-unsafe space that ML-driven perception creates, which 26262 alone does not address. Fleet intelligence that feeds any advisory or actuation path typically needs both; the combined evidence package is a major work item that must be scoped before model development, not after.
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+ Have we confirmed with an independent assessor whether ISO 21448 SOTIF applies to our ML outputs?+ Who owns the safety case — the carrier, the OEM, the telematics vendor, or the fleet operator?+ Is the evidence chain planned from day one, or are we retrofitting 26262 / 21448 after model training?Select the functional-safety regime the platform's outputs must satisfy.
Single choice
Define UNECE WP.29 R155 / R156 applicability
Why This Matters
UN Regulation No. 155 mandates a Cyber Security Management System across the vehicle lifecycle, and UN R156 mandates a Software Update Management System — both are type-approval prerequisites in UNECE 1958 Agreement markets including the EU, UK, Japan, and Korea. A connected-vehicle fleet intelligence platform that pushes model updates over the air is squarely in R156 scope. US-only fleets are not directly bound by WP.29 but global OEMs frequently impose the same controls contractually.
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+ Does our model-update pipeline meet R156 SUMS requirements for integrity, authenticity, and rollback?+ Have we mapped our platform risk analysis to R155 CSMS expectations, or are we relying on OEM CSMS?+ Is our CSMS/SUMS audit evidence ready for UNECE national type-approval authorities?Confirm whether the connected-vehicle platform must comply with UN R155 cybersecurity and R156 software-update regulations.
Select all that apply
Specify spectrum and connectivity envelope
Why This Matters
3GPP Release 18 and 19 are the 5G-Advanced stream that materially extended C-V2X sidelink capabilities, and C-V2X PC5 (3GPP TS 23.285) is the direct-mode channel that safety-critical V2V/V2I messaging depends on — it does not require the MNO uplink. CBRS Part 96 is the FCC framework under which private LTE/5G networks can be stood up for depot and yard operations without carrier integration. Picking the wrong layer for a given workload is how programs end up trying to deliver safety messaging over best-effort public MNO paths.
Note prompts — click to add
+ Which workloads can tolerate public MNO jitter, and which must ride C-V2X PC5 or CBRS private?+ Are we 3GPP Release 17 or Release 18+ ready, and does our roadmap include Release 19 vehicular enhancements?+ Have we validated C-V2X coexistence along the corridors we plan to serve?Confirm the wireless layer the platform depends on — drives both coverage and the C-V2X safety path.
Select all that apply
Specify deployment topology for the inference plane
Select the physical/logical deployment target for the fleet inference stack.
Single choice
Trinidy — For sub-50ms safety-adjacent inference along freight corridors, cloud inference is physically incompatible. Trinidy NEXUS OS provisions fleet inference across corridor tower sites; carrier provides edge density, fleet operators bring their algorithms, and T4 DevCo hardware is validated for continuous outdoor tower-site operation.
Define data residency and cross-border constraints
Why This Matters
Cross-border freight corridors between the US, Canada, and Mexico generate data that straddles privacy regimes in a way many fleet platforms have not reckoned with — PIPEDA in Canada and evolving Mexican data-protection rules do not automatically accept US retention defaults. Public-sector fleets (state DOT, municipal transit, federal GSA) also frequently add sovereignty requirements that exceed commercial baselines. Residency decisions made late are extraordinarily expensive to unwind.
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+ Have we inventoried every jurisdiction we operate in, or assumed US-only defaults?+ Does our data plane actually enforce residency, or only aspire to it?+ For public-sector fleets, do we have explicit sovereignty contracts?Map telemetry and model training data to jurisdictional constraints before architecture is finalized.
Select all that apply
Identify fleet operator platform integrations required
Why This Matters
Fleet operators rarely rip out their existing telematics platform to adopt a carrier AI service — integration with incumbents is the minimum viable product. Geotab and Samsara together account for a very large share of North American commercial fleet telematics, and both expose documented APIs. Picking integrations up front clarifies which event schemas, auth models, and retention policies the platform must honor, and which partnerships need to be papered before launch.
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+ Which integrations are we targeting for launch versus fast-follow, and who owns each partnership?+ Have we reviewed the API rate limits and webhook SLAs of our target platforms against our inference cadence?+ Are there OEM-native telematics (Ford Pro, Volvo Connect) we must also honor for mixed fleets?Select the fleet management platforms the inference output must integrate with.
Select all that apply
Define per-vehicle ROI envelope and subscription economics
Why This Matters
Published fleet-intelligence subscription pricing typically sits in the $15-$45/vehicle/month range, and at the top of that band the operator expects measurable fuel, maintenance, and safety savings — not just data. Without a per-vehicle ROI envelope, engineering tends to build features that cannot be priced, and sales ends up competing on connectivity price. The revenue per vehicle must also clear the cost of tower-edge inference capacity allocated to it.
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+ What fuel, maintenance, and insurance savings must the platform deliver per vehicle to defend our target price?+ Is connectivity sold separately from analytics, or bundled — and which drives the margin story?+ Do we have a holdout cohort that lets us prove ROI rather than infer it from telematics baselines?Quantify the per-vehicle revenue and operator-side savings the platform must defend.
Single choice