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Vehicle Research & Development
CAN / CAN-FD acquisition, decoding, and repeatable test workflows
AutoPi devices capture raw CAN/CAN-FD frames with hardware timestamps, run UDS / ISO 14229 diagnostics, and publish decoded signals using DBC or custom parsers. Data is time-aligned with GPS and IMU for track, bench, and durability studies. Configurations, parsers, and firmware are versioned to keep test runs fully reproducible.
Typical setup includes multiple CAN buses at different bit rates, acceptance filters per ID/mask, periodic PID/UDS polling at 1–10s intervals, and burst capture on trigger (DTC, threshold, or geofence entry). Devices buffer offline and sync on reconnection over signed, encrypted channels. Exports are available as CSV / JSON / MF4 for direct ingestion into analysis notebooks or processing pipelines.
Teams manage DBC libraries per VIN or program, promote templates across test environments, and audit every configuration change by author, timestamp, and diff. OTA jobs handle parser updates, firmware rollouts, and controlled rollbacks across instrumented vehicles,ensuring consistent and traceable software states.
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Advanced Vehicle Data
Configure CAN and CAN-FD buses with listen-only or active modes. Define filters, masks, and bitrate per channel. Store raw frames with monotonic timestamps, then decode into engineering units using DBC or custom extraction scripts. Attach run metadata (VIN, ECU, ambient, track segment) so results are comparable across iterations and drivers.
Support for R&D
Build repeatable test profiles: schedule UDS services, set sweep/step inputs, and pin diagnostic snapshots to events. Edge policies down-sample non-critical channels while preserving full-rate data around triggers for root-cause analysis. Export MF4/CSV/JSON with consistent naming, units, and sampling to eliminate manual clean-up before analysis.
Collaboration with Universities
Use role-based access for labs and cohorts. Share DBCs and templates per course or project, then compare outputs under identical conditions. All activities,command execution, config edits, parser changes,are logged for grading, provenance, and publication appendices.
Secure and Reliable Data
When cellular is unavailable, devices store locally without loss. On reconnection, data syncs using signed manifests and checksums. Per-device identity, firmware hash, and configuration revision are embedded with each dataset to guarantee traceable, tamper-evident runs.
CAN Data
Structured CAN / CAN-FD workflows for test and calibration
Configure CAN and CAN-FD interfaces for synchronized data acquisition across multiple buses. Schedule periodic requests, one-shot UDS diagnostics, and define triggers for burst capture on DTCs, threshold slopes, or specific map regions. Correlate signals with GPS and IMU data to evaluate acceleration, braking, cornering, and thermal stability during on-road or proving ground tests.
Maintain a central DBC library shared across projects, assigning signal definitions per bus, VIN, or program. Each derived signal includes scaling factors, offsets, units, and validity windows, ensuring that all downstream tools consume a uniform and structured timeseries. The result: seamless integration with existing calibration environments such as INCA, CANape, or MATLAB toolchains.
Typical workflows involve capturing high-frequency frames from critical ECUs, executing UDS routines for calibration validation, and merging results into MF4 or CSV datasets for analysis. With AutoPi, engineers can define and deploy data acquisition templates remotely, apply changes OTA, and monitor execution in real time through the cloud dashboard.
Insightful Data
From raw frames to analyzable signals
Build end-to-end pipelines that convert raw CAN/CAN-FD frames into time-aligned signal series enriched with contextual markers such as run start, lap count, or steady-state intervals. Each dataset carries structured metadata,VIN, ECU ID, parser version, and ambient conditions,so that results remain reproducible and comparable between test cycles.
Engineers can overlay key channels across multiple calibration runs, compute deltas per time segment, and automatically flag deviations from expected baselines. Combined with scheduled exports, results can be partitioned by VIN, trip, or test scenario, feeding continuous integration pipelines for simulation or validation.
OTA jobs manage parser updates, firmware rollouts, and log retention policies between test iterations. Each update includes signed manifests, execution logs, and success/failure codes, ensuring that test vehicles remain synchronized with the current experiment configuration. Rollbacks can be executed on-demand, allowing rapid iteration during calibration development or regression testing.
Smart Cities
Applied datasets for labs, pilots, and campuses
Publish sanitized datasets for mobility pilots: speed profiles, charge cycles, dwell time, stop events, and geofence interactions. Each dataset includes synchronized CAN/CAN-FD signals, GPS tracks, and IMU readings to capture both vehicle behavior and environmental context. Files are versioned, timestamped, and checksum-verified to maintain data integrity across multiple contributors.
Data can be shared via REST, MQTT, or scheduled exports in CSV, JSON, or MF4 formats. Access control follows strict tenant boundaries with scoped tokens and granular API keys. Partners can subscribe to specific event types,such as power consumption or brake energy recovery,and receive updates in near real-time for simulation or policy testing.
Typical deployments include university campuses, municipal pilot fleets, and EV charging research sites. Data from these sources feeds predictive models for energy demand, route efficiency, and infrastructure planning. Privacy is enforced through tokenized identifiers and aggregated event maps, enabling open collaboration without exposing individual trip data.
Combined with AutoPi’s device management and open APIs, Smart City stakeholders can iterate on real-world datasets,testing hypotheses, refining algorithms, and validating results with consistent, high-resolution data from mixed fleets and environments.
Something unclear?
Frequently asked questions
Answers for engineers and researchers using AutoPi in R&D, testing, data acquisition, and vehicle development environments.
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