Overview of the Intelligent Vehicles and Systems Group

1. Overview of the Intelligent Vehiclesand Systems Group Penn State University by Dr. S. Brennan See http://controlfreaks.mne.psu.edu for more info

2. An introduction to Sean Brennan • Youngest faculty with full appointment in ME, 5th year currently • Graduated from the University of Illinois at Urbana-Champaign • Experimentalist at heart, focus on chassis dynamics, systems engineering, and control • Service • Chair of ASME Automotive and Transportation Systems Committee • National Academies Transportation Visualization Committee • Organizer for: ASME DSCC conf, IEEE Conf on Control Applications • Faculty advisor for Penn State Robotics Club, AUVSI Competition • Teaching: • Department teaching award, 2006, College teaching award in 2007 • SAE Teetor award, 2008 • Research: • $3 million in ongoing research across 6 research labs • Support 10 to 15 grad students, 10 undergrad researchers • Selected as top papers at ’07 IFAC Advances in Automotive Control • Best paper in session, 2007 ASME IMECE

3. We do vehicle dynamics and control… That’s me doing a demonstration for my Vehicle Dynamics course, Spring 2008! See http://controlfreaks.mne.psu.edu for more info

4. Advanced estimation and virtual driving… See http://controlfreaks.mne.psu.edu for more info

5. Robotics and systems integration… See http://controlfreaks.mne.psu.edu for more info

6. Outline • Vehicle dynamics • Advanced estimation • Robotics and systems See http://controlfreaks.mne.psu.edu for more info

7. Vehicle Dynamics High-speed ground robots Passenger vehicle and hybrid vehicle control Heavy Vehicle Reliability 4 wheel steering Hybrid-electric military vehicles Jack-knifing scale vehicles See http://controlfreaks.mne.psu.edu for more info

8. 72-inch Roller chassis dynamometer Calibrated pavements and durability segments 1 mile oval Barrier-free roadside Handling area with 100 and 150 ft radius turning circles Half-mile, 4-lane straight-line segment for frequency-responses Full-scale vehicle dynamics testingThe facility: • PTI Test Track • One of a few closed-access University-owned test-track facilities • Built to accommodate passenger and heavy vehicles, • Only facility certified for bus chassis testing

9. Vehicle dynamics and model fitting “looking under rocks” Vishi, Sittikorn, John, Bridget, Ryan, Dennis,… • Experimental testing • We rarely trust other people’s models. Despite many claiming that they are “rock solid”, it’s the muddy fits that are of interest to us. • As a consequence, nearly every student in my group is trained in vehicle dynamic validation and data collection See http://controlfreaks.mne.psu.edu for more info

10. Model FittingFrequency Response – Roll Angle See http://controlfreaks.mne.psu.edu for more info

11. Model Fits – Time Domainlane change Yaw Rate Roll* Lateral Velocity Difficulty: hard Difficulty: medium Difficulty: easy * Assuming terrain influence is removed – more later See http://controlfreaks.mne.psu.edu for more info

12. Scale vehicle dynamics – dangerous scenarios “playing with toys” Sittikorn, Alexia, Andrew, Janine, Gareth… For many instances of vehicle testing, the use of a full-sized vehicle is costly and dangerous, and yet simulations are onerous and questionable to build One solution often used is a reduced-scale vehicle. Mathematics of dimensional analysis allows results to map between behaviors of a scaled vehicle and those of a full-sized vehicle.

13. Some examples 1:5 scale wheel-lift characterization 1:5 scale Platooning dynamics 1:8 scale autonomous motorcycle 1:14 scale jacknifing 1:8 scale vision-tracking

14. 1:5 scale • Multi-input system • Each axle is independently steered • Each wheel has independent torque control

15. See http://controlfreaks.mne.psu.edu for more info

16. Comparisons between vehicles… “our hobby is collecting vehicle data” Sittikorn, Mariona, Haftay, Dennis, Jon • Use same techniques as used in wind-tunnels, Buckingham Pi Theorem

17. Comparisons between vehicles… “our hobby is collecting vehicle data” Sittikorn, Mariona, Haftay, Dennis, Jon from publications from NHTSA database Outlier data

18. Advanced estimation Advanced sensor fusion Redundant estimation Vehicle-terrain interaction Path of Lidar Sensor Bridge with cement barriers on either side See http://controlfreaks.mne.psu.edu for more info

19. Road grade investigated for steady state circle at various speeds When aligned based on path distance covered, the road grade measurement is very repetitive irregardless of speed The influence of terrain Bridget

20. The influence of terrain Bridget Because feedback gains are directly related to modeling error, disturbance cancellation enables much higher gains and hence better tracking in closed-loop control.

21. Terrain as a sensor GPS was never meant to be trusted for feedback control Adam, Ryan, Vishi Off-line Localization using Pearson Correlation Coefficient By comparing pitch disturbances with a terrain map, we are able to resolve longitudinal position as good as 10 cm

22. Terrain as a sensor Learning as you go… Adam, Ryan, Vishi Representative visualization of your work Real-time Localization using Particle Filters Tested again at the track: we are able to resolve longitudinal position to 0.5 meters after traveling about 100 meters, with no GPS or other signals

23. Mapping terrain Pramod The goal of this work is to map road features and thereby correlate results to accident causation and eventually prevention Impact: 2000+ lives saved a year!

24. Mar ‘08 Oct ‘07 Nov ‘07 Apr ‘08 Dec ‘07 See http://controlfreaks.mne.psu.edu for more info

25. Mapping terrain • Shown at right is a banked curve from the test track • Getting 10 to 30 scans per second out to 80 meters of range. • Accuracy on the order of 6 cm at best case (perfect GPS). • Actual error is on the order of a meter or less. Path of Lidar Sensor Asphalt Roadway

26. Example bridge section Path of Lidar Sensor Bridge with cement barriers on either side AsphaltRoadway See http://controlfreaks.mne.psu.edu for more info

27. See http://controlfreaks.mne.psu.edu for more info

28. Advanced sensor fusion: how to utilize map-based position? • Can get orientation! • Real and virtual scenes are compared. • Preliminary results show orientation accuracies of 0.1 deg Vishi, Adam

29. See http://controlfreaks.mne.psu.edu for more info

30. Automation and systems integration High-speed ground robots Autonomous vehicle testing Hybrid-electric military vehicles 4 wheel steering See http://controlfreaks.mne.psu.edu for more info

31. Solving automation challenges… • Want to measure driver steering torque and backlash effects caused by steering systems, suspension, tire behavior, etc. • Problem: need standardized interface to measure driver inputs to the steering system and hence tire • Senior project?

32. Off-road modeling Preventing the accident in the first place Bridget, Jason Currently using Monte-Carlo methods and CarSim to analyze the effect of highway geometry on accident causation See http://controlfreaks.mne.psu.edu for more info

33. Predicting and preventing unintended roadway departure • According to FHWA, 60% of vehicle fatalities occurred after leaving the lane • High-gain control combined with terrain maps gives an unprecedented opportunity to mitigate this through the steering input

34. Efficiency improvements by sensor fusion “anticipating the road ahead” Nan, Alexia, Vishi See http://controlfreaks.mne.psu.edu for more info

35. Army is spending $30 million+ each month on premature battery failure Project: ultracapacitor switchover More reliable starts Vastly increase battery life Adaptive to extreme environmental temperatures (HIL) Test Stand Simulator Simulates HEMTT engine, alternator, battery and ultra-capacitor Responds to inputs from actual HEMTT starter motor Records speeds and torques of starter motor and engine HEMTT Starter SystemHIL Project See http://controlfreaks.mne.psu.edu for more info

36. Why is battery management necessary? For manned vehicles, reliability is important, but logistics and support costs are huge For unmanned vehicles, logistics and support costs are also important, but vehicle runtime and operator safety are paramount (example: EOD bots)

37. Campus-wide hardware-in-the-loop project

38. The goal of this work is to accelerate hybrid vehicle powertrain development Faculty Participants: Dr. Sean N. Brennan Lab/Center Name: GATE Hardware-in-the-Loop Sponsor: DOE • Distributed Powertrain System • Utilize campus-wide Ethernet • Incorporate existing labs • Integrate with industrial facilities Electric Motor IC Engine Chassis Dyno Flywheel Ultracapacitor Fuel Cell Battery Driving Simulator See http://controlfreaks.mne.psu.edu for more info

39. Analyzing reliability of PTI bus testing results Faculty Participants: Prof. Sean Brennan Lab/Center Name: Pennsylvania Transportation Institute Sponsor: Federal Transit Administration • Future Work: • Develop a predictive failure model to aid transit agencies in making purchase decisions • Collecting reliability data from transit agencies around USA • Comparing transit agency data with PTI test track data to assess their validity

40. See http://controlfreaks.mne.psu.edu for more info

41. See http://controlfreaks.mne.psu.edu for more info

42. See http://controlfreaks.mne.psu.edu for more info

43. Allometric Design and Stability Relationships for Explosive Ordinance Robots Participants: Brennan, Dean, Logan, Labs: Intelligent Vehicles and Systems Group, ARL, EDOG Sponsor: NAVEOD (DoD) • Real-time Localization using Particle Filters • By comparing inertial disturbances with a terrain map, we are able to resolve longitudinal position to 0.5 meters after traveling about 100 meters, with no GPS or other signals • Read more: • Guizzo, Erico. “$280 Million Robot Dustup,” IEEE Spectrum, p. 10-13, Vol. 44, No. 12, North American Edition, December 2007.

44. New frontiers • Recently initiated studies on human-vehicle interaction using a recently donated immersive driving simulator

45. New frontiers: remote semi-autonomy and driver assist Nan, Alexia, Vishi Use immersive driving simulator to remotely guide vehicles through pre-mapped terrain.

46. Thanks to supporters! The National Science Foundation – funded research into fundamentals of dynamic behavior through several student fellowships. (~$200k) The National Academy of Science, The Transportation Research Board – funded roadway scanning and terrain modeling (~$300k) Army TACOM – currently funding HIL work (~$1M) The Federal Transit Agency – funded test track and vehicle systems used on the track such as the DGPS/IMU system (track ~$14M, current project ~$300k) Naval Explosive Ordinance Disposal – currently funding robotics work (~$600k)

47. Questions? • Vehicle dynamics • Advanced estimation • Robotics and systems See http://controlfreaks.mne.psu.edu for more info