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In this paper, we investigate the performance of recently proposed driver-behavior modeling techniques for car-following task based on Gaussian mixture model (GMM) and piecewise auto regressive exogenous (PWARX) algorithms. Both driver-behavior modelings are employed to anticipate car-following driving behavior in terms of pedal control behavior (brake and gas/accelerator pedal operation) in response...
This paper presents a novel method to evaluate risk levels of driving behaviors based on acceleration patterns while braking. Acceleration patterns were recorded with drive recorders mounted on such vehicles as taxis and trucks to detect "events," i.e., remarkable scenes while driving such as rapid acceleration and deceleration. They also captured video images of the vehicles in front of...
We investigate the driving behavior differences at unsignalized intersections between expert and nonexpert drivers. By analyzing real-world driving data, significant differences were seen in pedal operations but not in steering operations. Easing accelerator behaviors before entering unsignalized intersections were especially seen more often in expert driving. We propose two prediction models for...
A signal processing approach for modeling vehicle trajectory during lane changing driving is discussed. Because individual driving habits are not a deterministic process, we developed a stochastic method. The proposed model consists of two parts: a dynamic system represented by a hidden Markov model and a cognitive distance space derived from the range distance distribution. The first part models...
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