Service Overview
Data Analysis & ML
Advanced EDA and ML service that accelerates automotive software development by 30–40% and reduces defects by 25–40%. Analyzes CAN-bus vehicle data to predict high-risk driving situations 3–15 seconds before critical incidents, bridging the gap between passive monitoring and active intervention — all without camera surveillance.
30–40%
faster dev cycles
25–40%
fewer defects
3–15s
hazard prediction
70%
AI rule coverage
35%
effort reduction
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
| Component | Technology | Purpose |
|---|---|---|
| Data Collection | CAN-bus Interface | Vehicle signal acquisition |
| Processing | Python, Pandas | Data preparation and analysis |
| ML Models | TensorFlow, PyTorch | Predictive modeling |
| Analytics | Scikit-learn, XGBoost | Statistical analysis |
| Visualization | Tableau, Grafana | Dashboard and reporting |
Use cases
Real-world applications
Documented outcomes from actual deployments.
MBD AI Platform — Rule Check Automation
AI-powered check-rule model automating 70% of model verification tasks in Model-Based Design workflows, replacing a 30–40 minute manual process per model.
Before
30–40 minutes manual rule checking per model, resource-intensive and error-prone
After
AI handles 70% of rule checks automatically in ~20 minutes per model
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
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
How we work
Implementation approach
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
Phase 2: Model Development
- Analyze historical vehicle data
- Develop predictive models for key use cases
- Validate model accuracy and performance
Phase 3: Integration & Deployment
- Integrate models with fleet management systems
- Deploy to vehicle fleet
- Set up monitoring and alerting systems
Phase 4: Optimization & Scaling
- Monitor model performance in production
- Collect feedback and improve models
- Scale to additional vehicle platforms
Explore more