Neura Parse supports data, workflow, simulation, edge-runtime, fleet, integration, and vehicle-lifecycle crypto-agility for mobility programs. We do not claim ISO 26262 compliance, production-driving performance, fleet scale, homologation, automated-driving readiness, or quantum-safe status without program-specific evidence.
Concept visualizationODD
Automation boundary
Target
Hardware benchmark
Staged
Update posture
Human
Exception owner
Sensors, ECUs, networks, real-time behavior, safety concepts, cybersecurity, update campaigns, fleet variation, data governance, service operations, and regulatory obligations all shape the usable capability.
01
Perception, planning, diagnostics, infotainment, connectivity, fleet, and safety functions have different timing, assurance, isolation, and update needs.
Acceptance questionAre responsibilities and failure containment explicit across compute, network, software, and safety mechanisms?
02
Weather, lighting, geography, road type, speed, sensor configuration, calibration, and vehicle condition change model behavior.
Acceptance questionDoes evaluation cover the intended ODD, sensor set, failure modes, and data distribution?
03
Firmware, models, maps, calibration, policies, and applications must remain compatible across vehicle variants and phased campaigns.
Acceptance questionCan the team target, verify, monitor, pause, and roll back an update by configuration?
04
Telemetry and incident data need purpose limitation, driver and passenger privacy, retention, consent or lawful basis, security, and operational value.
Acceptance questionIs every collected signal necessary, governed, secured, and tied to an owner and decision?
Vehicle intelligence moves through a chain that begins with requirements and scenarios and continues through models, sensors, ECUs, vehicle networks, target compute, fleet services, update infrastructure, incidents, and workshop operations. A result on a development machine says little about timing, thermal behavior, sensor variation, fault containment, or serviceability on the target platform. Each release needs to remain tied to the vehicle configuration and evidence that justified it.
Fleet operation adds another layer of controlled variation. Hardware generations, regional configurations, supplier components, calibration, connectivity, and usage patterns affect whether an update is compatible. OTA governance should define prerequisites, cohorts, authorization, staged rollout, installation proof, health monitoring, pause conditions, and rollback before a campaign begins. Telemetry then has a specific operational purpose rather than becoming an unbounded collection exercise.
The same lifecycle extends to cryptographic identity, secure boot, diagnostics, backend services, and software or firmware signing. Post-quantum readiness starts by mapping those trust paths, their data and certificate lifetimes, hardware constraints, supplier dependencies, and recovery mechanisms. Crypto-agility is the near-term engineering outcome: the ability to test and replace algorithms or components in stages without implying that an entire vehicle platform has already achieved quantum-safe status.
The loop connects engineering assumptions, target hardware, vehicle configuration, test results, update campaigns, incidents, and service feedback.
Define the function, ODD, architecture, interfaces, safety and security assumptions, data, and acceptance metrics.
Requirements · architecture · hazard and threat inputs · data plan
Exercise software, model, scenario, timing, network, sensor, and failure behavior through synthetic, SIL, or HIL environments.
Scenario · configuration · result · coverage · discrepancy
Integrate on target compute and vehicle, measure behavior, inspect edge cases, and trace findings back to requirements.
Target benchmark · test result · issue · requirement trace
Manage release, fleet targeting, staged update, health, incidents, rollback, and authorized improvement.
Campaign · installation proof · telemetry · incident · change record
Capabilities support development, integration, and crypto-agility planning. Safety integrity, driving performance, cybersecurity approval, vehicle homologation, production readiness, and quantum-safe status remain program-specific.
Connect scenarios, sensor data, labels, model versions, evaluation, edge cases, reviewer decisions, requirements, and release gates.
Package approved models for target compute with device benchmarks, release identity, health, isolation inputs, staged deployment, and rollback.
Connect telematics, dispatch, charging or depot activity, maintenance, incidents, driver or operator workflows, and business systems.
Model compatibility, cohorts, prerequisites, approvals, staged rollout, installation proof, monitoring, pause, rollback, and incident handling.
Connect scenario libraries, system models, synthetic data, SIL/HIL tests, physical results, discrepancies, and requirement evidence.
Inventory public-key cryptography across vehicle identity, diagnostics, telematics, backend services, PKI, secure boot, software and firmware signing, OTA, supplier components, and long-lived data; then frame a bounded migration pilot.
The reference architecture separates vehicle functions and safety mechanisms from fleet workflows while connecting evidence across development, update, service, and operation.
Camera, radar, lidar, ultrasonic, GNSS/IMU, diagnostics, domain controllers, gateways, and safety mechanisms remain platform-specific.
CAN/CAN FD · automotive Ethernet · SOME/IP · sensor and ECU interfaces
Model execution, preprocessing, local policy, health, logging, isolation inputs, and update agent run on defined target hardware.
NeuralOS where appropriate · accelerator runtime · device profile · watchdog
Secure telemetry, diagnostics, update, vehicle identity, cohort, command authorization, cryptographic inventory, supplier evidence, and offline reconciliation connect vehicle and operator.
Telematics · IAM/PKI · update and signing service · CBOM · event stream · privacy controls
NowFlow links incidents, service, campaign approval, fleet action, engineering review, customer systems, and evidence.
Fleet ops · service · issue · approval · ERP/CRM · data platform
Requirements, scenarios, models, cryptographic dependencies, test runs, target benchmarks, releases, incidents, supplier changes, and corrective actions remain traceable.
SIL/HIL · scenario · result · CBOM · requirement · release · rollback · CAPA
Profiles avoid invented vehicle counts, power figures, latency, safety integrity, or automated-driving performance. Those become program acceptance criteria.
Manage scenarios, sensor and label provenance, model comparison, edge-case review, target-device result, and requirement traceability.
Benchmark an approved perception or cabin model on target compute, monitor health and resource use, and exercise staged update and rollback.
Connect telematics, maintenance, route or depot events, incidents, work orders, and operator decisions with privacy and retention controls.
Exercise bounded perception, planning, operator supervision, fallback, and after-action review inside a defined low-speed or off-road ODD.
Trace one representative vehicle-to-backend trust path through provisioning, certificate lifecycle, secure boot, firmware signing, OTA authorization, supplier libraries, revocation, offline behavior, and recovery.
Automotive regulations and standards attach to a manufacturer, function, vehicle, market, and safety/cybersecurity process. They guide engineering here; they are not Neura Parse compliance badges.
The vehicle sponsor defines item, hazard analysis, safety concept, integrity targets, verification, validation, confirmation, and production evidence.
Address intended-function insufficiency, foreseeable misuse, triggering conditions, unknown scenarios, and validation where applicable.
Map cybersecurity and software-update management obligations, evidence, monitoring, incident, and change for applicable markets and vehicle types.
Anchor ML-KEM, ML-DSA, and SLH-DSA inventory, supplier evidence, interoperability testing, staged migration, and crypto-agility decisions across vehicle and backend lifecycles.
Define lawful purpose, minimization, access, retention, sharing, security, driver/passenger rights, and incident response.
External context is linked separately from Neura Parse capability claims.
Source 01 · NHTSA
Primary U.S. public context for automated-driving terminology, safety, development, and deployment considerations.
Source 02 · UNECE
Primary regulatory context for vehicle cybersecurity management systems in adopting markets.
Source 03 · UNECE
Primary regulatory context for software-update management systems and vehicle software updates.
Source 04 · NIST
Cross-sector reference for mapping, measuring, managing, and governing AI risk across the lifecycle.
Source 05 · NIST
Primary source for standardized ML-KEM, ML-DSA, and SLH-DSA plus migration guidance relevant to long-lived vehicle identity, signing, OTA, backend services, and supplier dependencies.
Service
Frame the function, ODD, data, architecture, regulation, evidence, and roadmap.
Service
Benchmark and package the workload for the target vehicle compute.
Service
Connect vehicle, fleet, update, service, simulation, and enterprise systems.
Service
Design scenarios, evaluation, human oversight, monitoring, and release gates.
Service
Inventory vehicle and backend trust dependencies and frame a standards-based pilot with supplier evidence and rollback.
We can scope data and evaluation workflows, edge runtime benchmarking, fleet integration, update governance, and a bounded prototype without claiming production readiness in advance.
