The U-M/Toyota Research Institute Partnership
Toyota Research Institute (“TRI”) entered into a research partnership with the University of Michigan. The TRI/UM partnership undertakes research in four thrusts: Autonomous Driving, Enhanced Automotive Safety, Indoor Mobility, and Accelerated Materials Design and Discovery.
TRI established an Ann Arbor office in 2016 because of the broad strengths of the university and the region, particularly in areas related to the emergence of high-level driver-assist systems, eventually leading to fully autonomous vehicles. TRI will also be near two well-established Toyota Technical Center campuses.
The partnership builds on Toyota’s strong and active presence in the Ann Arbor community. The two local Toyota Technical Centers have long worked with U-M on safety research. Toyota is a founding partner of UM’s Mcity, an interdisciplinary public-private research and development initiative that is developing the foundation for a commercially viable ecosystem of connected and automated vehicles.
Every year, TRI invites proposals from UM in the areas of machine intelligence applied to robotics, self driving cars, and automative safety. The research projects and faculty engaged in the TRI/UM partnership are listed below.
Click each button to read the project proposal summaries.
Trust, Control and Risk in Autonomous Vehicles
On funding for the Toyota Professorship in Artificial Intelligence:
“We are honored to fund this important new professorship," said Dr. Gill Pratt, Toyota Research Institute CEO and Toyota Motor Corporation Fellow. "AI is an extremely important tool in our toolbox and the University of Michigan is one of our most important collaborators in our joint quest to bring to market a car incapable of causing a crash."Dr. Gill Pratt, Toyota Research Institute CEO and Toyota Motor Corporation Fellow
“I get to live in both worlds and share what I know about what’s cutting edge and serve as a mentor and guide in the next frontier. I also have a lot of perspective in how industry operates, what the pace is like, and what research looks like thanks to this dual academic and corporate experience. I can easily speak to students about both."Ryan Eustice, Associate Professor, College of Engineering and Senior Vice President of Automated Driving at TRI
On Industry Funding for Girls Encoded:
"The benefit of having companies involved is that it shows students what they can do if they choose a career in CS. The challenge becomes one of investment. If we really want the needle to change in terms of diversity in the computer science field, it takes a lot of effort. It’s all hands on deck."Rada Mihalcea, Professor of Electrical Engineering and Computer Science
Project Manager, R&D and Engineering Management Division, Advanced R&D and Engineering Company, Toyota Motor Corporation
Chief Executive Officer of Toyota Research Institute (TRI) and a Fellow of Toyota Motor Corporation
Elmer G. Gilbert Distinguished University Professor Jerry W. and Carol L. Levin Professor of Engineering, Director of Robotics
Senior Director, U-M Business Engagement Center
Associate Director, Corporate and Foundation Relations, U-M College of Engineering
Anthony C. Lembke Department Chair of Chemical Engineering and John Werner Cahn Distinguished University Professor of Engineering
Director, Center for Ergonomics and COHSE Occupational Safety Engineering and Ergonomics Program and Professor, Industrial and Operations Engineering and Robotics
Postdoctoral Research Fellow of Mechanical Engineering & Mcity
- Bruder, D.,Sedal, A., Vasudevan, R., Remy, C.D. “Force Generation by Parallel Combinations of Fiber-Reinforced Fluid-Driven Actuators.” 2018. IEEE Robotics and Automation Letters.
- Chou, G., Sahin,Y. E., Yang, L., Rutledge,K. J., Nilsson, P., Ozay, N. “Using control synthesis to generate corner cases: A case study on autonomous driving.”2018. EEE Transactions on Computer-Aided Design of Integrated Circuits and Systems and EMSOFT 2018.
- Cutlip, S., Bhardwaj, A., Watts, L., Gillespie, R.B. 2018. “The Effects of Transition Type and Haptic Feedback on Shared Control of a Simulated Vehicle”. Haptic Symposium 2018.
- Ding, Y., Harirchi, F., Yong, S.Z., Jacobsen, E., Ozay, N. 2018. “Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles”. International Conference on Cyber-Physical Systems 2018.
- Hartley, Rl, Jadidi, M.G., Grizzle, J., Eustice, R. “Contact-Aided Invariant Extended Kalman Filter for Legged Robot State Estimation”. RSS 2018.
- Feng, F., Bao, S., Hampshire, R., and Delp, M. 2018. “Drivers Overtaking Bicyclists – An Examination Using Naturalistic Driving Data”. Accident Analysis & Prevention. 115 (2018). 98-109. DOI: 10.1016/j.aap.2018.03.010.
- Han, D., Huang, L., Panagou, D. 2018.”Approximating the Region of Multi-Task Coordination via the Optimal Lyapunov-Like Barrier Function”. American Control Conference 2018.
- Hartley, R. “Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation within Factor Graphs.” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018).
- Hartley, R., Mangelson, J. Gan, L., Ghaffari Jadidi, M., Walls, J., Eustice, R., Grizzle, J. 2018. “Legged Robot State-Estimation Through Combined Forward Kinematicand Preintegrated Contact Factors”. ICRA 2018.
- Huang, L., Panagou D. “Hierarchical Design of Highway Merging Controller using Navigation Vector Fields under Bounded Sensing Uncertainty”, 2018 Int. Symposium on Distributed Autonomous Robotic Systems (DARS), University of Colorado Boulder, October 2018.
- Jayaraman, S.K., Creech, C., Robert, L. P., Tilbury, D., Yang, X. J., Pradhan, A. and Tsui, K. 2018. “Trust in AV: An Uncertainty Reduction Model of AV-Pedestrian Interactions, Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction”. Proceedings of the Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2018), March 5–8, 2018, Chicago, IL, USA.
- Khorram, S., Jaiswal, M., Gideon, J., McInnis, M., Mower Provost, E. “The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild. Interspeech, September 2018.
- Li, S., Wang, W., Mo, Z., Zhao, D.”Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning”. IEEE Intelligent Vehicles Symposium, June 2018.
- Nilsson, P., Ozay, N. “Provably-Correct Compositional Synthesis of Vehicle Safety Systems”. Safe, Autonomous and Intelligent Vehicles, Unmanned System Technologies series. Springer. 2018.
- Peng, X., Liu, R., Murphey, Y.L., Stenty, S., Li, Y.“Driving Maneuver Detection via Sequence Learning from Vehicle Signals and Video Images”. Submitted to International Conference on Pattern Recognition, June, 2018.
- Rutledge, K., Yong, S.Z., and Ozay, N.“Optimization-Based Design of Bounded-Error Estimators Robust to Missing Data”. Proc. 6th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS), Oxford, UK. July 2018.
- Saund, B., and Berenson, D. “Motion Planning for Manipulators in Unknown Environments with Contact Sensing Uncertainty”. International Symposium on Experimental Robotics (ISER). November 2018.
- Sedal, A., Bruder, D., Bishop-Moser, J., Vasudevan, R., Kota, S. 2018. “A Continuum Model for Fiber-Reinforced Soft Robot Actuators”. ASME JMR 2018.
- Singh, K., Ding, Y., Ozay, N., and Yong, S.Z. “Input Design for Nonlinear Model Discrimination via Affine Abstraction”. Proc. 6th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS), Oxford, UK. July 2018.
- Singh, K., Shen, Q., Yong, S. 2018. “Mesh-Based Affine Abstraction of Nonlinear Systems with Tighter Bounds”. IEEE Conference on Decision and Control (CDC), December 2018.
- Spellings, M., Glotzer, S. C. “Machine learning for crystal identification and discovery”. AIChE J. 2018. DOI: 10.1002/aic.16157.
- Wan, Y., Cutlip, S., Ghasemi, A., Bardwaj, A., Gillespie, R.B., Sarter, R. 2018. “Haptic Shared Control: Improving Human-Automation Collaboration in Semiautonomous Driving”. Institute of Industrial and Systems Engineering 2018.
- Wang, W., Zhao, D. 2018. “Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications”. ICRA 2018.
- Wurts, J., Stein, J., Ersal, T. 2018. “Collision Imminent Steering Using Nonlinear Model Predictive Control”. American Control Conference.
- Wurts, J., Stein, J., Ersal, T. 2018.”Increasing Computational Speed of Nonlinear Model Predictive Control using Analytic Gradients of the Explicit Integration Scheme with Application to Collision Imminent Steering,” IEEE Conference on Control Technology and Applications, Copenhagen, Denmark, 2018.
- Zheng, Z., Oh, J., Singh, S. 2018. “On Learning Intrinsic Rewards for Policy Gradient Methods”. NIPS (Neural Information Processing Systems) 2018.
- Creech, C., Jayaraman, S.K., Robert, L. P., Tilbury, D., Yang, X. J., Pradhan, A. and Tsui, K. 2017. Trust and Control in Autonomous Vehicle Interactions, presented at the Morality and Social Trust in Autonomous Robots workshop at the 2017 Robotics: Science and Systems (RSS 2017), Cambridge, MA.
- Felt, W., Chin, K.Y., and Remy, C.D. 2017. “A Closed-Form Kinematic Model for Fiber Reinforced Elastomeric Enclosures“. ASME JMR.
- Felt, W., Remy, C.D.. 2017. “Modeling and Design of “Smart Braid” Inductance Sensors for Fiber-Reinforced Elastomeric Enclosures“. IEEE Sensors Conference and Dissertation.
- Feng, F., Bao, S., Delp, M. (accepted in 2017). “Evaluation of Drivers’ Lane Encroaching When Overtaking Bicyclists Using Naturalistic Driving Data – The Effects of Lane Marking Type, Bike Lane and Traffic”. Transportation Research Board.
- Hartley, R., Da, X., Grizzle, J.W. 2017. “Stabilization of 3D Underactuated Biped Robots: Using Posture Adjustment and Gait Libraries to Reject Velocity Disturbances“. IEEE Conference on Control Technology and Applications.
- Sedal, A., Bruder, D., Bishop-Moser, J., Vasudevan, R., Kota, S. (2018). “A Constitutive Model for Torsional Loads on Fluid-Drive Soft Robots“. ASME JMR 2017.
- Ushani, A.K., Wolcott, R.W., Walls, J.M., Eustice, R.M. 2017. “A Learning Approach for Real-Time Temporal Scene Flow Estimation from LIDAR Data”. ICRA.
- Zhang, B., Essl, G., Mower Provost, E. 2017. “Predicting the Distribution of Emotion Perception: Capturing Inter-rater Variability“. ICMI.