Service Overview

Data Analysis & ML

FPT Automotive's eda-and-ml service turns vehicle data and engineering drawings into actionable insights. It covers predictive CAN safety (hazards 3-15s ahead), driver emotional context (18.7x faster Qualcomm SoC inference), EV telemetry diagnostics (MLP + XGBoost + LLaMA for battery DTCs), and quality inspection (screw detection in 2s, part drawing reviews cutting setup time 70%). Processed privacy-first and on-device.

Machine LearningMBDModel Validation

18.7×

Qualcomm inference speedup

3-15s

CAN predictive safety warning

70%

Drawing review setup cut

2 seconds

Door panel screw inspection

35%

MBD platform manual savings

Capabilities

Key capabilities

Privacy-First Architecture

All analysis performed on-vehicle without transmitting sensitive information to cloud servers.

CAN Signal Analysis

Deep learning models trained on automotive CAN-bus data for predictive insights.

Anomaly Detection

Automatic identification of unusual vehicle behavior and potential failures.

Predictive Maintenance

Predict component failures before they occur, reducing downtime and maintenance costs.

Fleet Analytics

Aggregate insights across vehicle fleets for operational optimization.

Technology

Technology stack

Data Analysis & ML architecture diagram
Component Technology
Data Collection CAN-bus Interface
Processing Python, Pandas
ML Models TensorFlow, PyTorch
Analytics Scikit-learn, XGBoost
Visualization Tableau, Grafana

Use cases

Real-world applications

Documented outcomes from actual deployments.

1

Predictive Safety Layer for Accident Prevention

CAN-bus dynamics analysis to predict high-risk driving scenarios 3–15 seconds before potential incidents — targeting the 29% of rear-end collisions caused by driver inattention, without camera surveillance.

Before

Passive ADAS unable to anticipate subtle inattention risks; 29% of rear-end collisions from inattention

After

Proactive risk alerts 3–15 seconds ahead, bridging monitoring to active intervention

3–15s advance warning
29% collision social cost target
2

Driver Emotional Context AI System

Full-stack AI solution fusing 20+ CAN signals and driver behavior data on Qualcomm SA8255P SoC for real-time emotional context understanding and in-cabin personalization.

Before

No real-time driver state understanding; complex multi-signal fusion unresolved

After

Continuous inference at ≤100ms with 5.99% accuracy and 49.15% recall improvement

18.7× speed improvement
5.99% accuracy gain
3

AI Door Panel Inspection — 3 min to 2s

Machine learning model for automated screw inspection on car door panels, trained on defect patterns and integrated into production workflow. Returns immediate pass/fail per door at production speed.

Before

3-minute manual inspection per door throttling throughput on high-volume lines

After

AI inspection at 99.7% accuracy in 2 seconds per door

99.7% inspection accuracy
2s vs 3 min manual
$200K annual savings
4

AI-Powered Predictive Diagnostics for EVs

Multi-model diagnostic system combining MLP, XGBoost, and LLaMA to ingest real-time connected vehicle data (sensor, chassis, tire PSI, battery) and deliver predictive DTCs and battery health analysis via a conversational interface.

Before

Reactive diagnostics only; raw sensor data required specialist interpretation

After

Predictive fault warnings and battery diagnostics surfaced in natural language to vehicle owners

3 models MLP + XGBoost + LLaMA
4 streams sensor, chassis, tire, battery
5

AI Part Drawing Review — 70% Setup Time Cut

Deep learning pipeline combining YOLO/Faster R-CNN, OCR, and transformer models to automatically detect dimensional errors, tolerance violations, and annotation inconsistencies in automotive engineering drawings.

Before

Hours of manual expert review per drawing; complex multi-view drawings prone to missed errors

After

Automated review with color-coded markup, severity-classified error reports, and compliance dashboard

70% setup time reduction
85–90% precision target

How we work

Implementation approach

1

Phase 1: Data Strategy & Collection

  • Define data collection strategy and privacy requirements
  • Set up CAN-bus data collection infrastructure
  • Establish data governance and security protocols
2

Phase 2: Model Development

  • Analyze historical vehicle data
  • Develop predictive models for key use cases
  • Validate model accuracy and performance
3

Phase 3: Integration & Deployment

  • Integrate models with fleet management systems
  • Deploy to vehicle fleet
  • Set up monitoring and alerting systems
4

Phase 4: Optimization & Scaling

  • Monitor model performance in production
  • Collect feedback and improve models
  • Scale to additional vehicle platforms

Explore more

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AI-assisted end-to-end vehicle validation and HIL/SIL simulation compliance.