Spyropoulou I.K.,Laboratory of Transportation Engineering |
Karlaftis M.G.,National Technical University of Athens |
Reed N.,Transport Research Laboratory
Transportation Research Part F: Traffic Psychology and Behaviour | Year: 2014
In this paper we study driver behaviour changes when driving vehicles equipped with Intelligent Speed Adaptation (ISA) systems. The primary tool used is a driving simulator. Three different ISA human machine interface functionalities are investigated: informative, warning, and intervening. Data were extracted from the simulator along with questionnaires completed by drivers following each drive. Possible impacts of system functionalities on driver behaviour are studied through appropriate metrics including driving speed, speed deviation, frequency and magnitude of speeding and the empirical cumulative distribution function of speeding. Perceived impacts on drivers are investigated to identify driver attitudes towards the systems as well as possible relations between anticipated and measured behaviour. The study indicates that use of ISA systems, in general, results in the adoption of vehicle speeds that are likely to improve road safety. However, we also found that drivers may misuse ISA systems, potentially resulting in negative road safety effects. © 2014 Elsevier Ltd. All rights reserved.
Antoniou C.,National Technical University of Athens |
Antoniou C.,Laboratory of Transportation Engineering |
Kondyli A.,National Technical University of Athens |
Kondyli A.,Laboratory of Transportation Engineering |
And 4 more authors.
Transport and Telecommunication | Year: 2013
Most of the methodologies for the solution of state-space models are based on the Kalman Filter algorithm (Kalman, 1960), developed for the solution of linear, dynamic state-space models. The most straightforward extension to nonlinear systems is the Extended Kalman Filter (EKF). The Limiting EKF (LimEKF) is a new algorithm that obviates the need to compute the Kalman gain matrix on-line, as it can be calculated off-line from pre-computed gain matrices. In this research, several different strategies for the construction of the gain matrices are presented: e.g. average of previously computed matrices per interval per demand level and average of previously computed matrices per interval independent of demand level. Two case studies are presented to investigate the performance of the LimEKF under the different assumptions. In the first case study, a detailed experimental design was developed and a large number of simulation runs was performed in a synthetic network. The results suggest that indeed the LimEKF algorithm is robust and - while not requiring the explicit computation of the Kalman gain matrix, and thus having vastly superior computational properties - its accuracy is close to that of the "exact" EKF. In the second case study, a smaller number of scenarios is evaluated using a real-world, large-scale network in Stockholm, Sweden, with similarly encouraging results. Taking the average of various pre-computed Kalman Gain matrices possibly reduces the noise that creeps into the computation of the individual Kalman gain matrices, and this may be one of the key reasons for the good performance of the LimEKF (i.e. increased robustness).