Smart Cars that See (and Act)
Dariu M. Gavrila
Environment Perception
Research and Development
Behavior Design Meetup, 24-...
D. M. Gavrila
2
We originally thought Machine Intelligence would look like
1956 "Forbidden Planet"
Robby the Robot (Flickr)
D. M. Gavrila
3
Then more recently, some suggested it would be more like
2004 iRobot 1983-2009 Terminator
D. M. Gavrila
when in fact, Machine Intelligence is already with us,
and has a familiar embodiement …
The new Mercedes-Ben...
D. M. Gavrila
Sensors and Coverage (Mercedes-Benz E- and S-Class)
5
360°CAMERA
4 m range
D. M. Gavrila
6
Driver Assistance at Mercedes Benz (E- and S-Class, 2013)
Adaptive High Beam
Pre-Crash Braking (longitudin...
D. M. Gavrila
7
Challenge: Active Pedestrian Safety
D. M. Gavrila
8
Worldwide Traffic Fatalities 2010 –
Share of Vulnerable Road Users
Illustration: Bosch
D. M. Gavrila
Typical Scenario
9
D. M. Gavrila
10
Why is it difficult?
Large variation in pedestrian appearance (viewpoint, pose, clothes).
Dynamic and clu...
D. M. Gavrila
11
Better Algorithms: System Architecture
Pedestrian
Classification
Tracking / Fusion
Driver Warning /
Vehic...
D. M. Gavrila
12
Dense Stereo: Better ROIs, Classification and Localization
C. Keller, M. Enzweiler, M. Rohrbach, D.-F. Ll...
D. M. Gavrila
13
Lots of Real Data …
Ulm
Stuttgart
München
Aachen
Amsterdam
Parma
Brussels
Tokyo
Paris
Barcelona
San Franc...
D. M. Gavrila
14
EU WATCH-OVER (2008)
85%
50 km/h
10
Pedestrian Recognition Performance Last Decade (Downtown)
Correctly
r...
D. M. Gavrila
Driver Assistance at Mercedes Benz (E- and S-Class, 2013)
Adaptive High Beam
Pre-Crash Braking (longitudinal...
D. M. Gavrila
16
D. M. Gavrila
Will the Pedestrian Cross? (Through the Eyes of the Sytem)
17
D. M. Gavrila
Will the Pedestrian Cross? (Through the Eyes of the Driver)
18
D. M. Gavrila
(Future) Context-based Pedestrian Path Prediction
19
Preliminary experiments suggest that emergency braking ...
D. M. Gavrila
21
(Future) Automatic Evasion
C. Keller, T. Dang, A. Joos, C. Rabe, H. Fritz, and D.M. Gavrila. Active Pedes...
D. M. Gavrila
(Future) Autonomous Driving
22
Mercedes-Benz S500 Intelligent Drive Research Vehicle
(Full videoclip availab...
D. M. Gavrila
Final Remarks
Dramatic progress on vision-based pedestrian sensing, mirroring the success of
computer vision...
D. M. Gavrila
24
Better Algorithms + Market-Ready Hardware + More Data + Extensive Testing =
More Lives Saved.
D. M. Gavrila
25
Thank you
Credits
Markus Enzweiler, Christoph Keller, Stefan Munder, Jan Giebel
and others …
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BDAMS4 - Dariu Gavrila

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At our fourth edition of Behavior Design AMS meetup series April 24, 2014, we invited Dariu Gavrila to tell us how cars are recognising behavior now and in the future. Dariu works as research at the University of Amsterdam and at Daimler Benz.
http://www.meetup.com/Behavior-Design-AMS/events/173715882/

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BDAMS4 - Dariu Gavrila

  1. 1. Smart Cars that See (and Act) Dariu M. Gavrila Environment Perception Research and Development Behavior Design Meetup, 24-04-2014, Amsterdam
  2. 2. D. M. Gavrila 2 We originally thought Machine Intelligence would look like 1956 "Forbidden Planet" Robby the Robot (Flickr)
  3. 3. D. M. Gavrila 3 Then more recently, some suggested it would be more like 2004 iRobot 1983-2009 Terminator
  4. 4. D. M. Gavrila when in fact, Machine Intelligence is already with us, and has a familiar embodiement … The new Mercedes-Benz S Class (2013) 4
  5. 5. D. M. Gavrila Sensors and Coverage (Mercedes-Benz E- and S-Class) 5 360°CAMERA 4 m range
  6. 6. D. M. Gavrila 6 Driver Assistance at Mercedes Benz (E- and S-Class, 2013) Adaptive High Beam Pre-Crash Braking (longitudinal & lateral traffic) (Active) Lane Keeping Nightview AttentionTraffic Signs Parking Adaptive Cruise Control with Steering Assist (Active) Body Control S-Class only
  7. 7. D. M. Gavrila 7 Challenge: Active Pedestrian Safety
  8. 8. D. M. Gavrila 8 Worldwide Traffic Fatalities 2010 – Share of Vulnerable Road Users Illustration: Bosch
  9. 9. D. M. Gavrila Typical Scenario 9
  10. 10. D. M. Gavrila 10 Why is it difficult? Large variation in pedestrian appearance (viewpoint, pose, clothes). Dynamic and cluttered backgrounds. Pedestrians can exhibit highly irregular motion. Real-time processing required. Stringent performance requirements (especially for emergency maneuvres).
  11. 11. D. M. Gavrila 11 Better Algorithms: System Architecture Pedestrian Classification Tracking / Fusion Driver Warning / Vehicle Control Path Prediction & Risk Assessment Regions of Interest (Stereo, Motion, Geometry) Gavrila and Munder, „Multi-Cue Pedestrian Detection and Tracking from a Moving Vehicle”. Int. J. Computer Vision, 2007 S. Munder, C. Schnörr and D.M. Gavrila. “Pedestrian Detection and Tracking Using a Mixture of View-Based Shape- Texture Models.” IEEE Transactions on Intelligent Transportation Systems, 2008
  12. 12. D. M. Gavrila 12 Dense Stereo: Better ROIs, Classification and Localization C. Keller, M. Enzweiler, M. Rohrbach, D.-F. Llorca, C. Schnörr, and D.M. Gavrila. „The Benefits of Dense Stereo for Pedestrian Detection.“ IEEE Trans. on Intelligent Transportation Systems, 2011.
  13. 13. D. M. Gavrila 13 Lots of Real Data … Ulm Stuttgart München Aachen Amsterdam Parma Brussels Tokyo Paris Barcelona San Francisco … O(107) images O(106) labels
  14. 14. D. M. Gavrila 14 EU WATCH-OVER (2008) 85% 50 km/h 10 Pedestrian Recognition Performance Last Decade (Downtown) Correctly recognized pedestrians Number of falsely recognized pedestrian trajectories per hour 100% 50% 10 10000 100100 EU SAVE-U (2005) 65% 40 km/h EU PROTECTOR (2003) 40% 600 30 km/h N.B. # False alarms per hour << # Falsely recognized trajectories per hour DE AKTIV-SFR (2010) 90%
  15. 15. D. M. Gavrila Driver Assistance at Mercedes Benz (E- and S-Class, 2013) Adaptive High Beam Pre-Crash Braking (longitudinal & lateral traffic) Nightview AttentionTraffic Signs Parking with Pedestrian Recognition (Active) Body Control S-Class only 15 (Active) Lane Keeping Adaptive Cruise Control with Steering Assist
  16. 16. D. M. Gavrila 16
  17. 17. D. M. Gavrila Will the Pedestrian Cross? (Through the Eyes of the Sytem) 17
  18. 18. D. M. Gavrila Will the Pedestrian Cross? (Through the Eyes of the Driver) 18
  19. 19. D. M. Gavrila (Future) Context-based Pedestrian Path Prediction 19 Preliminary experiments suggest that emergency braking could be applied 0.5 s earlier, without increasing false alarms, reducing chances for hospital stay by 30% (TTC = 0.7s, vehicle speed = 50 km/h) 19
  20. 20. D. M. Gavrila 21 (Future) Automatic Evasion C. Keller, T. Dang, A. Joos, C. Rabe, H. Fritz, and D.M. Gavrila. Active Pedestrian Safety by Automatic Braking and Evasive Steering, IEEE Trans. on Intelligent Transportation Systems, 2011 300 ms from first sight of pedestrian to initiation of vehicle maneuver (braking or evasion)
  21. 21. D. M. Gavrila (Future) Autonomous Driving 22 Mercedes-Benz S500 Intelligent Drive Research Vehicle (Full videoclip available on YouTube)
  22. 22. D. M. Gavrila Final Remarks Dramatic progress on vision-based pedestrian sensing, mirroring the success of computer vision technology in driver assistance over the past decade. Perseverance pays off. Challenges still remain for pedestrian sensing. Additional focus: data fusion, situation understanding, new actuation concepts. Accident analysis at Mercedes-Benz indicates that the currently deployed pedestrian recognition technology could avoid 6 percent of pedestrian accidents and reduce the severity of a further 41 percent. 23 1999 2013
  23. 23. D. M. Gavrila 24 Better Algorithms + Market-Ready Hardware + More Data + Extensive Testing = More Lives Saved.
  24. 24. D. M. Gavrila 25 Thank you Credits Markus Enzweiler, Christoph Keller, Stefan Munder, Jan Giebel and others …

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