Hybrid Intelligence for Driver Assistance

We report on our on-going effort to build an adaptive driver support system, Driver Advocate TM , merging various AI techniques, in particular, agents, ontology, production systems and machine learning technologies. The goal of DA is to help drivers have a safer, more enjoyable, and more productive driving experience, by managing their attention and workload. This paper describes the overall architecture of the DA system, focusing on how we integrate agent and machine learning technologies to make it support the driver intelligently and unobtrusively. The architecture has been partially implemented in a prototype system built upon a high-fidelity driving simulator, letting us run human experiments. The human driving data collected from the simulator are used as input to machine learning tools to make DA learn to adapt to the unique driving behavior of each driver. Once the DA demonstrates the desired capabilities, it will be tested in a real car in an actual driving environment.

[1]  Frank Lindner,et al.  Autonomous Driving approaches Downtown , 1999 .

[2]  Jannes Aasman,et al.  Modelling driver behaviour in soar , 1995 .

[3]  John A. Michon,et al.  Soar: A Cognitive Architecture in Perspective , 1992 .

[4]  Rahul Sukthankar,et al.  Functional sensor modeling for automated highway system simulations , 1998, Other Conferences.

[5]  Jacek Malec,et al.  DRIVER SUPPORT SYSTEM FOR TRAFFIC MANOEUVRES , 1994 .

[6]  Milind Tambe,et al.  Agent Architectures for Flexible, Practical Teamwork , 1997, AAAI/IAAI.

[7]  Noel Massey,et al.  Safe Adaptation in an Automotive Vehicle: The Driver Advocate™ , 2002 .

[8]  Rahul Sukthankar,et al.  Multiple Adaptive Agents for Tactical Driving , 1998, Applied Intelligence.

[9]  Ross D. Shachter,et al.  Value-driven agents , 2001 .

[10]  Touradj Ebrahimi,et al.  Video-based multi-agent traffic surveillance system , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[11]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[12]  I. Dagli,et al.  Motivation-based approach to behavior prediction , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[13]  William M. Campbell,et al.  Machine Learning for Advising a Driver: A Survey , 2002, ICMLA.

[14]  Rahul Sukthankar,et al.  Situation Awareness for Tactical Driving , 1997 .

[15]  David J. Israel,et al.  Plans and resource‐bounded practical reasoning , 1988, Comput. Intell..

[16]  Jill Fain Lehman,et al.  A Gentle Introduction to Soar, an Architecture for Human Cognition. , 1996 .

[17]  Pravin Varaiya,et al.  Smart cars on smart roads: problems of control , 1991, IEEE Trans. Autom. Control..