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Automotive Log Analysis

Debug Android Automotive at Scale

From IVI crashes to CAN bus floods to VHAL failures — diagnose issues across the full vehicle software stack.

What We Analyze

Upload logs from IVI head units, digital clusters, ADAS modules, and CAN bus capture tools

Android Bugreports

Full system diagnostics from IVI, digital cluster, and ADAS modules running Android Automotive OS

CAN Bus Traces

.asc, .blf, .trc, .mf4, .csv, and candump formats — up to 10GB+ of vehicle bus data

VHAL & CarService Logs

Vehicle HAL property events, CarService dumps, and AAOS-specific framework diagnostics

Kernel & BSP Logs

Dmesg from vehicle BSPs — device tree issues, thermal zone events, driver probe failures

How to Export Your Logs

Capture logs from your vehicle development setup

ADB from Head Unit

  1. 1.Connect USB to IVI/head unit
  2. 2.Run adb bugreport
  3. 3.Upload the generated zip file

CAN Bus Capture

  1. 1.Use Vector CANalyzer, PEAK PCAN, or candump
  2. 2.Export as .asc, .blf, or .csv
  3. 3.Upload trace files to logcat.ai

VHAL Debug Dumps

  1. 1.Run dumpsys car_service on device
  2. 2.Capture VHAL property logs
  3. 3.Export as text file

Kernel Logs

  1. 1.Run dmesg on vehicle BSP
  2. 2.Capture boot and runtime logs
  3. 3.Upload for driver and thermal analysis

logcat.ai supports plaintext and structured CAN formats. Binary-only proprietary formats require export to a supported format first.

Why Automotive Debugging Is Hard

Vehicle software spans more layers than any other Android deployment

The Layer Nobody Can See

Between Android framework and vehicle hardware lies an invisible layer — CAN bus signals traverse ECUs, hardware abstractions, and framework services. No single existing tool connects the dots.

Multi-ECU Complexity

Issues span IVI head units, digital clusters, ADAS modules, and connectivity ECUs. A single bug can cascade across 4+ compute nodes running different software stacks.

CAN Bus at Scale

1-10 GB+ of CAN trace data with thousands of signals at 100 Hz across dozens of ECUs. Finding the needle in this haystack requires AI-powered correlation.

Safety-Critical Stakes

13.4 million vehicles were recalled for software issues in 2024 — half of all recalls. 55% of vehicle launches are delayed due to software integration. The cost of missed bugs is enormous.

Built for Automotive Engineers

AI-powered tools designed for the unique challenges of vehicle software debugging

Deep Research

An autonomous AI agent that investigates cross-layer vehicle issues. It traces CAN signals through VHAL to Android framework, correlates thermal events with driver behavior, and builds comprehensive root cause reports.

Delta for Automotive

Compare bugreports and CAN traces across BSP versions, OTA updates, and vehicle variants. Isolate regressions that appeared between firmware releases.

Learn more about Delta

Cross-Layer Correlation

Trace issues from CAN bus signals → VHAL property events → Android framework → kernel. Identify whether the root cause is in vehicle hardware, firmware, HAL, or application layer.

HW/SW Root Cause Separation

Automatically determine whether an issue originates in vehicle hardware, BSP firmware, Android framework, or application code — saving days of manual cross-team investigation.

Autonomous Investigation for Vehicle Issues

Deep Research traces issues across CAN bus, VHAL, and Android framework layers automatically

Vehicle Cross-Layer Investigation

Multi-step analysis across CAN, VHAL, and framework layers

"What caused the IVI reboot at 09:15?"
Parse CAN Signals
Trace VHAL Events
Correlate Framework Logs
Identify Root Cause
Cross-Layer Root Cause

CAN flood → VHAL storm → GPU OOM → reboot

CAN Bus • VHAL Property Callback Overflow
AI correlates CAN bus signals with VHAL events and Android framework logs

Cross-Layer Correlation with Delta

Upload CAN traces, bugreports, and kernel logs together. Delta correlates timestamps across all sources to find cross-layer root causes that single-file analysis would miss.

Delta Correlation Engine

Multi-file cross-layer analysis

Uploading...
CAN Bus Trace
09:15:01 0x18F: brake_pressure=0.8bar
09:15:02 0x1A0: engine_rpm=2400
09:15:03 0x3B0: camera_fault DTC=U0401
09:15:04 0x7DF: diag_req timeout
AAOS System Log
09:15:01 CarService: sensor_connected
09:15:02 VHAL: prop_update rear_cam
09:15:03 SurroundView: frame_drop x12
09:15:04 ClusterUI: ANR in rendering
ADAS ECU Log
09:15:01 ADAS: AEB armed v=65kph
09:15:02 ADAS: camera_feed latency 82ms
09:15:03 ADAS: camera_feed LOST
09:15:04 ADAS: AEB disengaged FAULT

Who Uses This

Automotive engineers across the vehicle software stack

IVI / Head Unit Engineers

Debug infotainment crashes, display failures, audio routing issues, and navigation hangs across Android Automotive head units.

AAOS Platform Teams

Investigate VHAL integration issues, CarService failures, thermal throttling, and BSP-level driver problems across vehicle variants.

HMI / Display Engineers

Debug UI rendering issues, touch input latency, display pipeline failures, and animation frame drops across vehicle displays and instrument clusters.

Vehicle Integration & QA

Validate OTA updates, compare pre/post-update logs with Delta, and catch regressions before they reach production vehicles.

Frequently Asked Questions

Common questions about automotive log analysis

logcat.ai supports .asc (Vector), .blf (BLF binary), .trc (PEAK), .mf4 (MDF4/ASAM), .csv (generic columnar), and candump text output. We handle files up to 10GB+ with thousands of signals across dozens of ECUs.

Yes. Delta accepts CAN traces alongside Android bugreports and kernel logs. It correlates timestamps across all sources to trace issues from vehicle bus signals through VHAL to Android framework events — finding cross-layer root causes automatically.

For Android logs, standard ADB is sufficient. For CAN bus data, any tool that exports to supported formats works — Vector CANalyzer, PEAK PCAN-View, candump, or any MDF4-compatible tool. VHAL and CarService logs can be captured with standard Android dumpsys commands.

logcat.ai works with any Android Automotive OS deployment — from IVI head units to digital clusters to ADAS modules. We support ARM and x86 architectures, and our kernel analysis covers vehicle-specific BSPs from major silicon vendors.

Deep Research is an autonomous AI agent that investigates vehicle issues step by step. It parses CAN signals, traces VHAL property events, correlates framework logs, and searches for known issues — building a comprehensive root cause report with citations in 5-10 minutes.

Upload bugreports from before and after an OTA update. Delta compares them side by side, identifying new crashes, changed behavior, performance regressions, and driver failures that appeared between firmware versions. It's particularly useful for BSP updates where regressions can be subtle.

Yes. All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Your log data is never used to train AI models. We use AI APIs with zero-data-retention agreements. Enterprise customers can get dedicated cloud instances with custom data retention policies.

Start Analyzing Your Vehicle Logs

See how logcat.ai can reduce your automotive debugging time from days to minutes. Full access during your 30 day pilot.