Collaborators: Prof. Francesco Borrelli, Mechanical Engineering, UC Berkeley
Funding: NSF (#1239323), Hyundai
Researchers: Katherine Driggs Campbell
To develop smarter active safety systems that rely on driver monitoring to predict how humans will behave
To collect data in a safe environment, a 4-axis motion simulator has been setup for human-in-the-loop driving experiments
To implement autonomous framework in a heterogeneous environment, given the current infrastructure, vehicle sensors, and V2V communication technology
High-level decision making considers work in control theory and hybrid systems, communication, and artificial intelligence. Data-driven, probabilistic models to take into account uncertainties and variations in driving styles
Driver assistance systems (e.g. lane change assistance, lane keeping assistance), autonomous driving (learn driver’s preferences)
K. Driggs-Campbell, R. Bajcsy, Identifying Modes of Intent from Driver Behaviors in Dynamic Environments. In IEEE International Conference on Intelligent Transportation Systems (ITSC), September 2015.
V. Shia,Y. Gao, R. Vasudevan, K. Driggs-Campbell, T. Lin, F. Borrelli, R. Bajcsy, "Semi-Autonomous Vehicular Control Using Driver Modeling,” IEEE Transactions on Intelligent Transportation Systems, To Appear.
D. Sadigh, K. Driggs-Campbell, A. Puggelli, W. Li, V. Shia, R. Bajcsy, A. Sangiovanni-Vincentelli, S.S. Sastry, S. A. Seshia. “Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior,” AAAI Spring Symposium on Formal Verification & Modeling in Human-Machine Systems 2014.
K. Driggs-Campbell, V. Shia, R. Vasudevan, R. Bajcsy, “Probabilistic Driver Models for Semiautonomous Vehicles,” in Digital Signal Processing for In-Vehicle Systems, 2013.