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

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.