Driver Behavior Intelligence

Smarter | Safer | Secure

AIDMS

Driver Behavior Intelligence

Smarter | Safer | Secure

Trending

Trending

For enhanced road safety, regulators and automotive safety bodies are increasingly incorporating ADAS features like driver tracking in their roadmaps. Many players in the industry are working to enhance monitoring accuracy by detecting and predicting driver condition, behavior, and health., Machine learning, artificial intelligence, and sensor fusion technologies are being combined to achieve optimal performance.

The next frontier in driver and road safety is being explored: a connected Driver Monitoring System with AI-powered algorithms (AIDMS) that easily correlates vehicle data with the driver’s health/state data. This helps to implement predictive diagnostics in vehicles and orchestrate actions to automate driver engagement and detect aggressive drivers. 

Trending
Opportunities & Challenges

Opportunities & Challenges

Opportunities & Challenges

OEMs and manufacturers in the automotive industry are investing a lot to develop robust DMS platforms and applications built on passive and active monitoring systems. Existing vehicle systems do not provide sufficient data to understand the driver’s exhaustion level and orientation to safe or aggressive driving, and fuel economy.

Accurate prediction of driver behavior is difficult since there is no linkage between the vehicle’s prognostics and telematics systems with passive safety systems that enable vision-based driver monitoring.

Developing ML-based analytics for diagnostic, prescriptive, predictive, causal, and inferential analytics using neural networks, support vector machines, or associations & sequence discovery algorithms can help to find patterns. Correlating data obtained from driver monitoring systems with predictive diagnostics of vehicles can help orchestrate actions for better engagement, thus enriching the driving experience.

Service Framework

AIDMS
Core Features
  • Driving Event analytics
  • Distracted Driving detection
  • Driver/passenger detection
  • Driver Scoring
  • Driver Drowsiness
  • Driver Posture analysis
Service Features
  • On-Board real-time analytics
  • Weather independent detections
  • Multiple deployment models – On Board / Edge / Cloud (Public/Private)
  • Detection with any camera (IR / RGB / Stereo)
  • TE Proprietary neural network algorithms
  • Integrated solution for DMS with vehicle data correlation

 

Cloud Services
  • Analytics on DMS alerts 
  • Vehicle telematics data analytics
  • Driver behavior analysis using OBD data

Insights:

  • Driver DNA
  • Trip analytics
  • Dangerous driving event detection
  • Usage-based Insurance
  • Fleet analytics & live tracking
  • Personalized In Car Entertainment

Differentiators

Technology

A hybrid approach for correlation of vision & telematics data

Detection Reliability

Face detection – 85%, Drowsiness – 93%, Scoring – 87%, Occupant action detection – 90%

Flexibility

OBD / TCU device-agnostic application

Detection with any camera (IR / RGB / Stereo)

In-Vehicle system control for navigation, multimedia, vehicle functions, etc.

Uniqueness 

Edge analytics for DMS - On Board real-time analytics

Advanced neural network algorithms

Integrated solution for DMS with vehicle data correlation

 

Benefits to the Customer

  • Reduce time-to-market by up to 50%
  • Algorithm accuracy of over 90% for face detection, eye-gaze, and head-pose detection already achieved.
  • Easily customizable to suit customer needs

IR based Intelligent Driver Monitoring System for Japanese Tier1

Developed an AI algorithm for DMS, which could work in extremely dark conditions and with IR cameras. The AI-based DMS was optimized on different embedded platforms with different frameworks and algorithms for achieving the highest accuracies.

Neural Network Based Occupant Detection System
Case Study

Neural Network Based Occupant Detection System

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