Modeling Differences in Behavior Within and Between Drivers

  • Andrew M. Liu
Conference paper


A new generation of driver assistance systems such as advanced collision warning and intelligent brake assist are now available options for the modern automobile. However, the addition of each new system increases the information load on the driver and potentially detracts from their ability to safely operate the vehicle. Over 10 years ago, we [Pentland A, Liu A (1999) A modeling and prediction of human behavior, Neural Comput 11, 229–242] suggested that a car that could infer the current intent of the driver would be able to appropriately manage the suite of systems and provide task relevant information to the driver in a timely fashion. This “smart car” would observe the driver’s pattern of behaviour in terms of their control of the vehicle then infer their current driving task using a Markov Dynamic Model. The approach could recognize driver actions from their initial behaviour with high accuracy under simulated driving conditions. Since that time new computational approaches and improved in-vehicle technology (e.g., GPS technology, advanced radar and video/computer vision, etc.) have moved the realization of this concept further along. Yet, one fundamental question still needs to be carefully addressed: Can these driver models, built on statistical descriptions of driver behaviour, accurately model the differences between drivers or changes within an individual driver’s behaviour? In this paper, I describe some examples of these differences and discuss their potential impact on a model’s ability to consistently recognize behaviour. To ensure the acceptance of the next generation driver assistance systems, these issues will have to be resolved.


Markov Dynamic Model Individual differences Driving style Driver experience Fatigue 


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Copyright information

© Springer-Verlag Italia Srl 2011

Authors and Affiliations

  1. 1.Man Vehicle Laboratory, Department Of Aeronautics and AstronauticsMassachusetts Institute of TechnologyCambridgeUSA

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