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Human Computer Intreaction


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About a car simulator research study paper that talks about the finding how car simulators make life easy for novice drivers.

About a car simulator research study paper that talks about the finding how car simulators make life easy for novice drivers.

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  • 1. Car Driver Skills Assessment using Posture Recognition Presented By: sumit kadyan
  • 2. Authors • Madalina –Ioana Toma (Transilvania University of Brasov) • Leon J.M. Rothkrantz (Delft University of technology) • Csaba Antonya (M.I.Toma)
  • 3. Need For it • Difficult to learn driving in real life scenario • Safety issues • Time boundation • Learning driving as a Novice is a key in driving career
  • 4. Introduction • Recognition of driving posture with High Accuracy • Feedback mechanism for novice drivers using Alarm system. • Experiment conducted in real time.
  • 5. What Sets it apart? • Recognition of complete body parts. • Use of “Markerless” Sensors. • Provides accurate measurement of joint configuration and rapid movements of hands.
  • 6. Simulation Environment framework KINECT EYELINK2 CLIPS LOGITECH G27 TORCS PC
  • 7. Pictorial Representation
  • 8. Framework Components • Kinects-sensing upper body movements • Torcs-3D car simulator • Clips-For rule based expert system • Eyelink 2 device –for sensing eye and gaze movement
  • 9. Markerless Sensor
  • 10. Markerless Sensor • Uses pattern recognition principle • Monitor process quality via control panel or via Ethernet • Reproducibility of 0.6 mm • Plug can be rotated 90° • High scanning speed of 7 m/s
  • 11. KINECT
  • 12. KINECT Sensor • RGB camera sensor • Configuration is done using Sdk tool by windows • IR Emitter and IR depth Sensor • Used for tracking upper body movements
  • 13. EYELINK II
  • 14. Eye link 2 • High resolution and data rate • Head mounted video-based eye tracker. • Used for tracking eyes movement and head orientation • Two eye cameras allow binocular eye tracking
  • 15. CLIPS • C Language Integrated Production System. • CLIPS incorporates a complete object- oriented language(COOL) for writing expert systems. • COOL combines the programming paradigms of procedural, object oriented and logical (theorem proving) languages. • Provides High Portability.
  • 16. Logitech G27 • Provides simulation Environment with TORCS. • USB Interface.
  • 17. TORCS virtual environment
  • 18. TORCS • 3D car simulator supporting input devices( steering wheels, joystick, game pads etc.) • Provides connection, configuration and synchronization. • Written in C++ and open source avaliable under GPL license • Easy to add/create content • Excellent performance and stability
  • 19. Related Work • Pose Estimation • Gaze Detection • Focused only on Expert Drivers. • Analyses done using offline techniques like silhouettes, bounding boxes.
  • 20. How it Works? • Takes real time parameters from sensors and environment. • Refers to an expert rule based system to determine the driving postures and give feedback ,also sound an alarm if the novice driver posture is wrong. • Uses the clips inference engine • Matching takes place between current state of fact list and list of instances
  • 21. Description of the rules
  • 22. Defining Rules • Rules for recognizing driving postures are stored in the knowledge base system. • Rules for driving posture:  DP1,DP2,DP3,DP4  DP1-Left hand postures  DP2-Right hand postures  DP3-Eye and Head postures  DP4-leg postures
  • 23. Working • Each group represnts a postuers runsin paralles with the other • A driver posture is represnted a key poses • Which is a combination of 2- 5 key poses • These are the inputs to the CLIPS • In a driving task the driving posture used to perform that maneuver are defined in a specific order
  • 24. Finite state machine diagram
  • 25. DFSM • Determisnntic finite state machine • , S, s0 , , F • -Input alphabet(from the sensors) • S-Finite set of states (showing transition in DP1 ,DP2 …DP4 • s0- Initial state(When the system is calibrated for start ) • -state transition(from one • F-final state
  • 26. Diagram of the interface
  • 27. Experiment • Experiment was focused on developing a assistive intelligent system for indoor training of novice drivers • Experiments conducted in laboratory with proper lighting for sensors • 2 kind of experiments • One for robustness and performance of posture recognition the novice driver without traffic • 2 in is the complete framework evaluation.
  • 28. Two Experiments Experiments Conducted Detecting Robustness and accuracy of posture recognition for novice drivers Complete framework evaluation and provide feedback
  • 29. Participants • 12 participants • 8 males and 4 females • All having driver license • With little or no experience
  • 30. Results of Experiment 1 • Every subject performed the postures for 10 times • Driving postures recognition rate achieves 96.4% accuracy • Driving posture stability achieves 96.21% accuracy • GOOD” and “WORST” messages
  • 31. Table of Results
  • 32. Experiment 2 : Rules • driver needs to start the car (StC) • driver wants to drive away (DA) • driver keeps the lane (KL) • driver increases the speed (IS) or decreases the speed (DS) based on traffic signs • driver wants to take over (TO) or change lane (CL) • driver wants to make a forward parking (FP) driver wants to stop the car (SpC).
  • 33. Results of Experiment 2 • In the StC situation we achieved 88% correct postures detectioni • In the IS and DS speed variation situations we achieved an accuracy of 100%. • A lower accuracy of less than 70% we obtained in TO and FP
  • 34. Results experiment 2 • In the StC situation we achieved 88% correct postures detection. • In the IS and DS speed variation situations we achieved an accuracy of 100%. • A lower accuracy of less than 70% we obtained in TO and FP
  • 35. Table of Results
  • 36. Conclusion • To improve the take over and forward parking by combining probabilistic methods reducing uncertainty of certain driver postures.
  • 37. References • Toma, Madalina-Ioana; Rothkrantz, Leon J.M.; Antonya, Csaba, "Car driver skills assessment based on driving postures recognition," Cognitive Infocommunications (CogInfoCom), 2012 IEEE 3rd International Conference on , vol., no., pp.439,446, 2-5 Dec. 2012 • I. Lefter, L.J.M. Rothkrantz, P. Bouchner, P. Wiggers: “A multimodal car driver surveillance system in a military area”, Driver Car Interaction & Interface, 2010. • Y.F. Lu, and Ch.Li: “Recognition of Driver Turn Behavior Based on Video Analysis”, Journal of Advanced Materials Research Vol. 433-44, pp 6230- 6234, 2012. • D.B. Kaber, Y. Liang, Y. Zhang, M. L. Rogers, and S. Gangakhedkar: “Driver performance effects of simultaneous visual and cognitive distraction and adaptation behavior”, Journal of Transportation Research Part F 15, pp. 491– 501, 2012.