Trace Failures Across the Full Vehicle Software Stack
AI that reconstructs vehicle behavior across CAN bus, ECUs, VHAL, and Android layers. Upload a bugreport and a CAN trace, ask what happened, get root cause in minutes.
Why Vehicle Software Debugging Is Hard
Failures cross invisible boundaries between vehicle hardware, ECU firmware, VHAL, and the Android stack — no single tool sees the full chain
Invisible Boundaries Between Layers
A CAN bus signal crosses ECU boundaries, passes through vehicle HAL abstractions, and surfaces as an Android framework event. The failure chain spans 4+ layers — and no existing tool connects them.
Distributed Vehicle Software System
Issues cascade across IVI head units, digital clusters, ADAS modules, and connectivity ECUs. A single root cause in one compute node triggers symptoms across the entire vehicle software system.
CAN Bus at Scale
1-10 GB+ of CAN trace data with thousands of signals at 100 Hz across dozens of ECUs. Correlating this with Android logs, VHAL dumps, and kernel events manually is a multi-day effort.
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.
See It in Action: IVI Reboot Investigation
A real failure chain that spans the full vehicle software stack — from CAN bus to system crash. The kind of issue that takes days to debug manually.
The Failure Chain
Manual Investigation
~3 daysOpen CANalyzer to find the CAN flood. Switch to ADB logcat to find VHAL overload. Grep dmesg for GPU buffer errors. Cross-reference timestamps manually across 4 different tools. Escalate across CAN, platform, and framework teams.
With logcat.ai
~5 minutesUpload the bugreport and CAN trace. Ask "Why did the IVI reboot?" Deep Research autonomously traces the chain: CAN flood → VHAL overload → GPU exhaustion → OOM → system_server crash. Full root cause with citations.
From Fragmented Tools to Unified Intelligence
Vehicle software debugging today requires stitching together insights from disconnected tools. logcat.ai replaces that workflow.
Before: Manual Cross-Tool Correlation
- Vector CANalyzer or PEAK PCAN for CAN bus trace analysis
- ADB logcat for Android framework and application logs
- dmesg and kernel console for BSP and driver events
- Vendor-specific tools for ECU diagnostics and VHAL dumps
- Manual timestamp correlation across all sources, often in spreadsheets
Days of work across multiple teams to connect the dots
After: Upload and Ask
- Upload bugreport + CAN trace to logcat.ai
- Ask "Why did the IVI reboot?" or "What caused the VHAL timeout?"
- Autonomous investigation traces the full chain across every layer and delivers root cause with citations
Root cause in minutes, with the full cross-layer evidence chain
Built for the Distributed Vehicle Software System
AI-powered tools that understand the full vehicle stack — from CAN bus signals to Android framework events
Powered by 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 — without human guidance.
Delta for OTA Regression Detection
Compare bugreports and CAN traces across BSP versions, OTA updates, and vehicle variants. Isolate regressions that appeared between firmware releases — catch what changed before it reaches production.
Learn more about DeltaCross-Layer Correlation
Trace issues from CAN bus signals → VHAL property events → Android framework → kernel. The AI reconstructs the full failure chain across every boundary in the vehicle software stack.
HW/SW Root Cause Separation
Automatically determine whether an issue originates in vehicle hardware, BSP firmware, Android framework, or application code — ending the cross-team blame game in minutes instead of days.
Autonomous Investigation for Vehicle Issues
Deep Research traces issues across CAN bus, VHAL, and Android framework layers automatically — no manual correlation required
Vehicle Cross-Layer Investigation
Multi-step analysis across CAN, VHAL, and framework layers
CAN flood → VHAL storm → GPU OOM → reboot
OTA Regression Detection with Delta
Upload CAN traces, bugreports, and kernel logs from before and after an OTA update. Delta correlates timestamps across all sources and isolates exactly what changed — and what broke.
Delta Correlation Engine
Multi-file cross-layer analysis
Part of the logcat.ai Platform
Automotive analysis is built on the same AI platform that powers Android debugging for device manufacturers, telecom, and IoT teams worldwide.
Autonomous multi-step investigation that traces issues across every layer of the vehicle stack.
Compare logs across OTA versions, vehicle variants, and BSP releases to isolate regressions.
Automated analysis across CPU, memory, network, power, storage, security, and more.
Who Uses This
Automotive engineers across the distributed vehicle software system
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.
Supported Log Types
Upload logs from IVI head units, digital clusters, ADAS modules, and CAN bus capture tools
Full system diagnostics from IVI, digital cluster, and ADAS modules running Android Automotive OS
.asc, .blf, .trc, .mf4, .csv, and candump formats — up to 10GB+ of vehicle bus data
Vehicle HAL property events, CarService dumps, and AAOS-specific framework diagnostics
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.Connect USB to IVI/head unit
- 2.Run adb bugreport
- 3.Upload the generated zip file
CAN Bus Capture
- 1.Use Vector CANalyzer, PEAK PCAN, or candump
- 2.Export as .asc, .blf, or .csv
- 3.Upload trace files to logcat.ai
VHAL Debug Dumps
- 1.Run dumpsys car_service on device
- 2.Capture VHAL property logs
- 3.Export as text file
Kernel Logs
- 1.Run dmesg on vehicle BSP
- 2.Capture boot and runtime logs
- 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.
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.