Intelligent Driver Assistant System using Hidden Markov
Decision Processes
Professor Djamel Bouchaffra (Advisor)
Sheng H. Kung (Ph.D. Student)
Computer Science & Engineering 131 Dodge Hall
Phone: 248-370-2242, email: [email protected]
Intelligent Driver Assistant System
Road Scene
Recognition and
Interpretation
Subsystem
Interpretation
Result
Sensor
Input
Vehicle
Advice
Generation
Subsystem
Advice,
Warning
Driver
Driving
Goal of Driving,
Preferences in
driving
Goal


To Explore Hidden Markov Decision
Process (HMDP) to Build an Adaptive
System (Learning Machine) that Senses
Driving Environment, Interprets It and
Performs Decisions (Act Upon It)
The System Will Assist Driver React by
Giving Advices Using Voice or Sending
Out Warning Signals
Challenge

Driving Environment is Dynamic
and Unpredictable






Traffic Volume and Multiple Lanes
Driver Behavior
Lighting
Road Conditions and Road Signs
Speed Limit
Weather Condition
Strategies

Scene Recognition




Feature Extraction
Objects Segmentation and Classification
Objects Positioning and Tracking
Scene Representation


States/Events Discovery
Flexible Hierarchical/Structured HMM

Scene Interpretation

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Reaction Decision Making

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Scenario Capture
Abstraction and Semantic Levels HMM
Markov Decision Process
Navigation Advice Searching and
Output