Driver Behavior Intelligence

Smarter | Safer | Secure

AIDMS

Driver Behavior Intelligence

Smarter | Safer | Secure

Trending

Trending

Regulators and automotive safety bodies are increasing including ADAS features like driver monitoring in their roadmaps for improved road safety.

Many industry players are trying to improve the monitoring accuracies for detection and prediction of not only driver state but also driver behavior and health. They are using ML and AI with sensor fusion to optimize the system performance.

A connected Driver Monitoring System with AI-powered algorithms that easily correlate the vehicle data and driver health/state data to identify aggressive drivers, predictive diagnostics of vehicles, orchestrating actions to automate driver engagement, etc. is the next frontier in driver and road safety that is been explored.

 

Trending
Opportunities & Challenges

Opportunities & Challenges

Opportunities & Challenges

Automotive OEMs and suppliers are focusing heavily on creating a comprehensive DMS platform built on a combination of features and capabilities across passive and active monitoring systems.

Development of vision-based driver monitoring systems as a passive safety feature in separation from vehicle prognostics/telematics systems, which is a challenge for true driver behavior intelligence due to the absence of correlations between vision and telemetry data.  

For example, existing systems can not infer whether the driver is economical, the risk for others, or fatigued, etc. with the current parameters which are available from the vehicle.

Developing ML-based analytics using neural networks, support vector machines or associations & sequence discovery algorithms for diagnostic, prescriptive, predictive, causal, and inferential analytics which can find patterns in this data and easily correlate to identify aggressive driver, predictive diagnostics of vehicle, orchestrating actions to automate driver engagement, etc. is a big challenge.

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 Enterntainment

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 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 Studies

Neural Network Based Occupant Detection System

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