This paper presents an integrated human driver model for a closed-loop simulation of the intelligent safety system. A lateral human driver model was developed to represent the steering behavior of a human driver using the finite preview optimal control method. A longitudinal human driver model represents a human driver’s throttle and brake control behavior relative to the preceding vehicle moti...

Urban traffic flow is a complex system. Behavior of an individual driver can have butterfly effect which can become root cause of an emergent phenomenon such as congestion or accident. Interaction of drivers with each other and the surrounding environment forms the dynamics of traffic flow. Hence global effects of traffic flow depend upon the behavior of each individual driver. Due to several a...

The authors present a cyber-physical systems study on the estimation of driver behavior in autonomous vehicles and vehicle safety systems. Extending upon previous work, the approach described is suitable for the long term estimation and tracking of autonomous vehicle behavior. The proposed system makes use of a previously defined Hybrid State System and Hidden Markov Model (HSS+HMM) system whic...

This paper describes recent advances in the analysis and classification of driver behavior in actual driving scenarios. We employ data obtained from the UTDrive corpus to model driving behavior and to detect if distraction due to secondary tasks is present. Hidden Markov Models (HMMs) are used to capture the sequence of driving characteristics acquired from the vehicle’s CAN-Bus (Controller Are...

Driving support and cruise assist systems are of growing importance in achieving both road traffic safety and convenience. Such driver support seeks to achieve, with the highest possible quality, nothing less than “driver-vehicle symbiosis under all conditions.” At the same time, many traffic accidents result from improper driver behavior. The author focuses on driver behavior under various dri...

This research is focused on driver behavior in traffic, especially during car-following situations and safety critical events. Driving behavior is considered as a human decision process in this research which provides opportunities for an artificial driver agent simulator to learn according to naturalistic driving data. This thesis presents two mechine learning methodologies that can be applied...

The driver gap acceptance and rejection behavior during mandatory lane changes on a multilane freeway are analyzed in this paper. Gaps are accepted or rejected based on comparison with a minimum value generally defined as the critical gaps. Critical gaps are estimated based on the accepted and rejected gaps observed in the field. Driver behavior can be classified as consistent or inconsistent o...

This paper argues that the driving task exhibits the properties of complex systems. Driving behavior emerges from the intricate and complex interactions between the driver, the vehicle and the environment. An emerging driving behavior could not necessary be linearly predicted. However a context-aware system could assist the driver in augmenting the probability of undertaking safe behavior. Unli...