Improving human-in-the-loop decision making in multi-mode driver assistance systems using hidden mode stochastic hybrid systems

Existing commercial driver assistance systems, including automatic braking systems and lane-keeping systems, may monitor the state of the vehicle or the environment to determine whether the systems should intervene. However, the state of the human driver is not typically included in the decision making process. In this paper, we propose to use hidden mode stochastic hybrid systems to model the interaction between the human driver and the vehicle. We show that by monitoring the human behavior as well as the vehicle state, we can infer the human state and enhance the quality of decision making in a driver assistance system. The resulting control policy is obtained by solving an optimal planning problem of the proposed hidden mode hybrid system. The policy can automatically balance the decision making about when to give warning to the driver and when to actually intervene in the control of the vehicle.

[1]  A. Galip Ulsoy,et al.  Identification of driver state for lane-keeping tasks , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[2]  Pascal Poupart,et al.  Point-Based Value Iteration for Continuous POMDPs , 2006, J. Mach. Learn. Res..

[3]  Ho Gi Jung,et al.  A New Approach to Urban Pedestrian Detection for Automatic Braking , 2009, IEEE Transactions on Intelligent Transportation Systems.

[4]  Luke Fletcher,et al.  Driver Inattention Detection based on Eye Gaze—Road Event Correlation , 2009, Int. J. Robotics Res..

[5]  Laurent Delahoche,et al.  An evidential fusion architecture for advanced driver assistance , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Sterling J. Anderson,et al.  An optimal-control-based framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios , 2010 .

[7]  Thao Dang,et al.  Stochastic situation assessment in advanced driver assistance system for complex multi-objects traffic situations , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Mohan M. Trivedi,et al.  Looking-in and looking-out vision for Urban Intelligent Assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[9]  S. Shankar Sastry,et al.  Experimental Design for Human-in-the-Loop Driving Simulations , 2014, ArXiv.

[10]  Ruzena Bajcsy,et al.  Semiautonomous Vehicular Control Using Driver Modeling , 2014, IEEE Transactions on Intelligent Transportation Systems.

[11]  Ruzena Bajcsy,et al.  Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control , 2014 .

[12]  Allen Y. Yang,et al.  An efficient algorithm for discrete-time hidden mode stochastic hybrid systems , 2015, 2015 European Control Conference (ECC).

[13]  Ruzena Bajcsy,et al.  Improved driver modeling for human-in-the-loop vehicular control , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).