Engineering use cases

Automotive log analysis for real validation decisions.

AurigaTrace turns raw vehicle logs into traceable project evidence for validation, calibration, diagnostics, ADAS, connected-vehicle pilots, and engineering report reviews.

9

use-case families

rules

threshold evidence

reports

review output

Use-case command center

Evidence-to-decision flow

traceable
01

Project

vehicle program or validation campaign

02

Logs

CSV, JSONL, ASC + DBC active; MF4 preview; BLF roadmap

03

Signals

sample count, min, mean, max, windows

04

Rules

thresholds, duration gates, severity

05

Report

review evidence and AI-assisted narrative

Use-case portfolio

Cover the core automotive engineering workflows from one governed workspace.

Each use case starts with raw evidence, moves through parser and signal processing, and ends with findings, reports, or AI-assisted narratives that remain tied to the source project and log file.

durability and release readiness

Vehicle validation

CSV, MF4, GPS, event markers

Compare signal behavior across variants, campaigns, and release candidates.

  • baseline runs
  • variant comparison
  • release evidence
control limits and operating windows

Calibration review

CSV, MF4, calibration exports

Identify threshold breaches, sustained-duration events, and operating-window drift.

  • min/avg/max review
  • duration gates
  • calibration references
EV charging and energy transfer

Charging system validation

charging CSV, current, voltage, thermal data

Review AC/DC charging profiles, current behavior, thermal limits, and fault events.

  • charge profile checks
  • thermal limits
  • fault correlation
energy storage and degradation

Battery endurance

battery voltage, current, SOC, pack temperature

Track endurance trends, cell/pack limits, SOC behavior, and thermal exposure.

  • SOC trend review
  • pack thermal checks
  • endurance reporting
bus trace and DBC readiness

CAN network analysis

ASC + DBC active, BLF/CAN FD roadmap

Move from text trace intake to decoded signal review and network anomaly findings.

  • message timing
  • DBC signal mapping
  • period drift rules
scenario and event evidence

ADAS highway validation

scenario CSV, GPS, CAN, ROS bag roadmap

Tie sensor/control/event logs to scenario evidence and review-ready findings.

  • scenario windows
  • event alignment
  • report traceability
safety and dynamic response

Chassis brake events

brake pressure, wheel speed, steering, stability control

Review threshold events and correlated signals for stability-control evidence.

  • brake pressure peaks
  • wheel-speed deltas
  • finding severity
software, HMI, and service behavior

Infotainment diagnostics

Android logcat, DLT, service logs, DTC exports

Connect software logs and service events with diagnostic findings and report notes.

  • logcat review
  • service failures
  • DTC correlation
connected vehicle operations

Fleet telemetry pilot

CSV, JSON, GPS, DTC, operational telemetry

Validate ingest patterns, fleet metrics, and repeatable reporting for pilot programs.

  • fleet summaries
  • telemetry quality
  • pilot reports

Decision workflow

From test question to review-ready conclusion.

The same flow supports a charging campaign, CAN trace review, software diagnostic event, or ADAS scenario: define the question, register the evidence, normalize it, evaluate rules, and attach the conclusion to a report.

01

Define engineering question

Vehicle program, campaign, scenario, or diagnostic concern.

02

Register evidence

Upload raw logs, preserve source metadata, and assign project scope.

03

Normalize signals

Use parser capability to produce signals, statistics, and windows.

04

Evaluate rules

Run thresholds, duration gates, and severity logic against statistics.

05

Review findings

Inspect observed values, rules, status, and engineering impact.

06

Prepare report

Generate review-ready report evidence and controlled AI draft context.

Use-case matrix

Map each engineering domain to logs, workflows, and outputs.

A strong log platform should make it obvious which log families are active today, which parser work is next, and what decision artifact each domain produces.

Current platform fit

CSV signal statistics, JSON/JSONL telemetry summaries, ASC + DBC foundation parsing, project rules, findings, reports, sample projects, and the format registry are already usable. BLF and MF4/MDF are the next strongest implementation slices for deeper network and measurement use cases.

Use caseLog evidencePlatform workflowDecision output
ValidationCSV / MF4 / GPSsignal statistics + rule findingsvariant readiness
Calibrationmeasurement channelsoperating windows + duration gatesthreshold review
Chargingvoltage / current / thermalprofile checks + fault correlationcharging evidence
BatterySOC / pack temperaturetrend review + endurance summarydurability report
NetworkASC + DBC / BLF roadmapactive text trace parser + timing rulesCAN anomaly review
ADASscenario / event / GPSevent alignment + report contextscenario evidence
DiagnosticsDLT / logcat / DTCsoftware event reviewservice finding
FleetJSON / CSV / telemetrypilot ingest + KPI reportingfleet summary

Engineering cockpit

Review signal behavior, rule evidence, and domain context together.

Engineers do not need another static dashboard. They need a traceable cockpit that connects signal windows, observed values, thresholds, finding status, and report readiness.

signals

processed evidence

rules

threshold checks

findings

review status

reports

decision record

Use-case cockpit

Scenario signal review

5 signals
threshold event
scenario window aligned
SignalObservedContextState
battery_current-12.7 Aregen windowreview
motor_temp_c67.1 Cthermal bandok
brake_pressure86.0 barlimit breachwarning
can_period_ms12.7 msperiod driftwarning
vehicle_speed88.4 km/hscenario speedok

Team views

Different engineering teams share one evidence chain.

Validation, calibration, diagnostics, and report-review users can work from the same project data without losing role boundaries or evidence provenance.

Validation engineer

Inspect project runs, compare signals, evaluate thresholds, and build evidence for release decisions.

Calibration engineer

Review operating limits, duration gates, control-window drift, and repeatability across test runs.

Diagnostics engineer

Correlate software logs, DTC exports, bus events, and service behavior into reviewable findings.

Report reviewer

Use approved statistics, rule findings, and controlled AI drafts to prepare consistent reports.

Start with a real vehicle log use case

Login to inspect the current sample workspace, or submit a pilot request with your project, log families, parser needs, and report workflow.