Human Behavior Based Predictive Brake Assistance

Driver assistance systems have both the potential to alert the driver to critical situations and distract or annoy the driver if the driver is already aware of the situation. As systems attempt to preemptively warn drivers more and more in advance, this problem becomes exacerbated. We present a predictive braking assistance system that identifies not only the need for braking action, but also whether or not a braking action is being planned by the driver. Our system uses a Bayesian framework to determine the criticality of the situation by assessing (1) the probability that braking should be performed given observations of the vehicle and surround and (2) the probability that the driver intends to perform a braking action. We train and evaluate our system using over 22 hours of data collected from real driving scenarios with 28 different drivers

[1]  Shang-Jeng Tsai,et al.  The study on intelligent vehicle collision-avoidance system with vision perception and fuzzy decision making , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  Mohan M. Trivedi,et al.  Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[3]  Mohan M. Trivedi,et al.  Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning , 2005, IEEE Transactions on Intelligent Transportation Systems.

[4]  Michael A. Goodrich,et al.  Designing human-centered automation: trade-offs in collision avoidance system design , 2000, IEEE Trans. Intell. Transp. Syst..

[5]  Wassim G. Najm,et al.  EXAMINATION OF CRASH CONTRIBUTING FACTORS USING NATIONAL CRASH DATABASES , 2003 .

[6]  Charles Elkan,et al.  Boosting and Naive Bayesian learning , 1997 .

[7]  F. Biral,et al.  Combining safety margins and user preferences into a driving criterion for optimal control-based computation of reference maneuvers for an ADAS of the next generation , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[8]  Qiang Ji,et al.  An automated face reader for fatigue detection , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Alex Pentland,et al.  Graphical models for driver behavior recognition in a SmartCar , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[10]  Azim Eskandarian,et al.  Research advances in intelligent collision avoidance and adaptive cruise control , 2003, IEEE Trans. Intell. Transp. Syst..

[11]  Dario D. Salvucci.  Modeling Driver Behavior in a Cognitive Architecture , 2006, Hum. Factors.

[12]  J. Hillenbrand,et al.  Situation assessment algorithm for a collision prevention assistant , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[13]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[14]  Tomohiro Yamamura,et al.  A Driver Behavior Recognition Method Based on a Driver Model Framework , 2000 .